Link for documentation : https://convokit.cornell.edu/documentation/tutorial.html
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import convokit
from convokit import Corpus, download
from convokit import TextParser
from convokit import Classifier
from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import Perceptron
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
import spacy
from convokit import VectorClassifier
from sklearn.model_selection import train_test_split
import warnings
from sklearn import metrics
warnings.filterwarnings('ignore')
Import Politeness Strategies Transformer in Convokit
1) For more information and a quick demo refer the following:
2) For documentation regarding Politeness Transformer refer following resoures provided by Convokit
https://convokit.cornell.edu/documentation/politenessStrategies.html
from convokit import PolitenessStrategies
wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))
Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus
# Represent Corpus as a dataframe using get_utterances_dataframe()
df_wiki_corpus = wiki_corpus.get_utterances_dataframe()
df_wiki_corpus.head()
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'Where', 'tag': 'W... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'Thanks', 'tag': '... | [] |
| 627353 | NOT_RECORDED | Sir i think u hav many friends on wiki who can... | user | None | 627353 | -0.247941 | 0 | {'A233ONYNWKDIYF': 17, 'A2UFD1I8ZO1V4G': 17, '... | [{'rt': 2, 'toks': [{'tok': 'Sir', 'tag': 'NNP... | [] |
| 448565 | NOT_RECORDED | I can't find it. Maybe I didn't manage to gue... | user | None | 448565 | 0.058298 | 0 | {'A233ONYNWKDIYF': 17, 'A1TLLJDX8H4JP1': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',... | [] |
| 625810 | NOT_RECORDED | I can't spend too much time, and I'm no specia... | user | None | 625810 | 0.346093 | 0 | {'A21753FQKCM5DQ': 17, 'AYG3MF094634L': 14, 'A... | [{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',... | [] |
from convokit import TextParser
parser = TextParser(verbosity = 2000)
The corpus will be annotated with Politeness strategies and Politeness markers. The Politeness strategies is essentially like a dictionary which stores feature politeness keys(like Please, Please start etc). If suppose the text contains Please, using spacy in the background convokit will tokenize the sentences and if suppose word please is in the text, the feature politeness will assign Please == 1. This will help the classfier to assign the text to the correct class. Text Parser will will first obtain dependency parses for the utterances, and then check for strategy use and then PS Strategies will be used
For instance : Politenes_strategies stores politeness strategy usage and 'politeness_markers'keeps positive occurances of each politeness strategies.It also stores the location where the word occurs
'politeness_strategies': {'featurepoliteness==Please==': 0, 'featurepoliteness==Please_start==': 0, 'featurepoliteness==HASHEDGE==': 0, 'featurepoliteness==Indirect_(btw)==': 0, 'featurepoliteness==Hedges==': 0, 'featurepoliteness==Factuality==': 0,....}
'politeness_markers': {'politenessmarkers==Please==': [], 'politenessmarkers==Please_start==': [], 'politenessmarkers==HASHEDGE==': [], 'politenessmarkers==Indirect_(btw)==': [], 'politenessmarkers==Hedges==': [], 'politenessmarkers==Factuality==': [], 'politenessmarkers==Deference==': [], 'politenessmarkers==Gratitude==': [], 'politenessmarkers==Apologizing==': [], 'politenessmarkers==1st_person_pl.==': [], 'politenessmarkers==1st_person==': [[('i', 0, 1)]], 'politenessmarkers==1st_person_start==': [], 'politenessmarkers==2nd_person==': [[('you', 0, 25)], [('you', 1, 1)]], 'politenessmarkers==2nd_person_start==': []}
The above 21 Linguistic markers are presented in [5] and below :
https://www.cs.cornell.edu/~cristian/Politeness_files/politeness.pdf
wiki_corpus = parser.transform(wiki_corpus)
2000/4353 utterances processed 4000/4353 utterances processed 4353/4353 utterances processed
# Update dataframe
def update_dataframe(df_object,corpus):
df_object = corpus.get_utterances_dataframe()
return df_object
update_dataframe(df_wiki_corpus,wiki_corpus).head()
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'Where', 'tag': 'W... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'Thanks', 'tag': '... | [] |
| 627353 | NOT_RECORDED | Sir i think u hav many friends on wiki who can... | user | None | 627353 | -0.247941 | 0 | {'A233ONYNWKDIYF': 17, 'A2UFD1I8ZO1V4G': 17, '... | [{'rt': 2, 'toks': [{'tok': 'Sir', 'tag': 'NNP... | [] |
| 448565 | NOT_RECORDED | I can't find it. Maybe I didn't manage to gue... | user | None | 448565 | 0.058298 | 0 | {'A233ONYNWKDIYF': 17, 'A1TLLJDX8H4JP1': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',... | [] |
| 625810 | NOT_RECORDED | I can't spend too much time, and I'm no specia... | user | None | 625810 | 0.346093 | 0 | {'A21753FQKCM5DQ': 17, 'AYG3MF094634L': 14, 'A... | [{'rt': 3, 'toks': [{'tok': 'I', 'tag': 'PRP',... | [] |
from convokit import PolitenessStrategies
ps = PolitenessStrategies()
# Set markers to true so that markers are visible
wiki_corpus = ps.transform(wiki_corpus, markers = True)
#update corpus
update_dataframe(df_wiki_corpus,wiki_corpus).head()
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 627353 | NOT_RECORDED | Sir i think u hav many friends on wiki who can... | user | None | 627353 | -0.247941 | 0 | {'A233ONYNWKDIYF': 17, 'A2UFD1I8ZO1V4G': 17, '... | [{'rt': 2, 'toks': [{'tok': 'sir', 'tag': 'NNP... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 448565 | NOT_RECORDED | I can't find it. Maybe I didn't manage to gue... | user | None | 448565 | 0.058298 | 0 | {'A233ONYNWKDIYF': 17, 'A1TLLJDX8H4JP1': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'i', 'tag': 'PRP',... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 625810 | NOT_RECORDED | I can't spend too much time, and I'm no specia... | user | None | 625810 | 0.346093 | 0 | {'A21753FQKCM5DQ': 17, 'AYG3MF094634L': 14, 'A... | [{'rt': 3, 'toks': [{'tok': 'i', 'tag': 'PRP',... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
# Calculates strategy prevalence and plot graph if plot == True, with an optional selector that specifies
# which utterances to include in the analysis.
data = ps.summarize(wiki_corpus)
data
| Averages | |
|---|---|
| Please | 0.038594 |
| Please_start | 0.006662 |
| HASHEDGE | 0.497128 |
| Indirect_(btw) | 0.004824 |
| Hedges | 0.188146 |
| Factuality | 0.057202 |
| Deference | 0.013784 |
| Gratitude | 0.066391 |
| Apologizing | 0.019986 |
| 1st_person_pl. | 0.102458 |
| 1st_person | 0.607627 |
| 1st_person_start | 0.291293 |
| 2nd_person | 1.022743 |
| 2nd_person_start | 0.053067 |
| Indirect_(greeting) | 0.084999 |
| Direct_question | 0.217551 |
| Direct_start | 0.092120 |
| HASPOSITIVE | 0.653113 |
| HASNEGATIVE | 0.461980 |
| SUBJUNCTIVE | 0.096715 |
| INDICATIVE | 0.065932 |
plot = ps.summarize(wiki_corpus, plot = True)
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-225-fbe6cbe509d4> in <module> ----> 1 plot = ps.summarize(wiki_corpus, plot = True) ~\anaconda3\lib\site-packages\convokit\politenessStrategies\politenessStrategies.py in summarize(self, corpus, selector, plot, y_lim) 128 :return: a pandas DataFrame of scores with graph optionally outputted 129 """ --> 130 scores = self._get_scores(corpus, selector) 131 132 if plot: ~\anaconda3\lib\site-packages\convokit\politenessStrategies\politenessStrategies.py in _get_scores(self, corpus, selector) 112 113 for utt in utts: --> 114 for k, v in utt.meta[self.marker_attribute_name].items(): 115 counts[k[21: len(k)-2]] += len(v) 116 scores = {k: v/len(utts) for k, v in counts.items()} ~\anaconda3\lib\site-packages\convokit\model\convoKitMeta.py in __getitem__(self, item) 16 17 def __getitem__(self, item): ---> 18 return dict.__getitem__(self, item) 19 20 @staticmethod KeyError: 'politeness_markers'
# Train classifier on only those records which have reponse -1 or 1
# iter_utterances() -> Iterates over the utterances in the corpus
list_filtered = []
for utterances in wiki_corpus.iter_utterances():
if(utterances.meta["Binary"]!= 0):
list_filtered.append(utterances)
wiki_corpus_binary = Corpus(utterances = list_filtered)
wiki_corpus_binary.get_utterances_dataframe()[0:3]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 214411 | NOT_RECORDED | |style="vertical-align: middle; padding: 3px;"... | user | None | 214411 | 0.619247 | 1 | {'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A... | [{'rt': 20, 'toks': [{'tok': '|style="vertical... | {'feature_politeness_==Please==': 1, 'feature_... | {'politeness_markers_==Please==': [[('please',... | [] |
convokit_clf = Classifier(obj_type = 'utterance', pred_feats = ["politeness_strategies"],
labeller=lambda utt: utt.meta['Binary'] == 1)
Initialized default classification model (standard scaled logistic regression).
Cross Validation is performed to check how well our model will perform on unseen data
convokit_clf.evaluate_with_cv(wiki_corpus_binary)
Using corpus objects... Running a cross-validated evaluation... Done.
array([0.75229358, 0.76376147, 0.75 , 0.78390805, 0.77241379])
print("Mean Value for cross-validation evaluation Standardized Logistic ",np.mean(convokit_clf.evaluate_with_cv(wiki_corpus_binary)))
Using corpus objects... Running a cross-validated evaluation... Done. Mean Value for cross-validation evaluation Standardized Logistic 0.7681514288727196
wiki_corpus_binary.get_utterances_dataframe()[0:3]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 214411 | NOT_RECORDED | |style="vertical-align: middle; padding: 3px;"... | user | None | 214411 | 0.619247 | 1 | {'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A... | [{'rt': 20, 'toks': [{'tok': '|style="vertical... | {'feature_politeness_==Please==': 1, 'feature_... | {'politeness_markers_==Please==': [[('please',... | [] |
# size_of_corpus = wiki_corpus_binary.get_utterances_dataframe().shape[0]
# split_percent = 0.955
# limit = int((split_percent)*size_of_corpus)
# train_utt_id = wiki_corpus_binary.get_utterance_ids()[0:limit]
# test_utt_id = wiki_corpus_binary.get_utterance_ids()[limit:0]
def split_dataset(filtered_corpus, split_percent, size):
limit = int((split_percent)*size)
train_utt_id = filtered_corpus.get_utterance_ids()[0:limit]
test_utt_id = filtered_corpus.get_utterance_ids()[limit:]
train_corpus = Corpus(utterances = [utt for utt in filtered_corpus.iter_utterances() if utt.id in train_utt_id ])
test_corpus = Corpus(utterances = [utt for utt in filtered_corpus.iter_utterances() if utt.id in test_utt_id])
return train_corpus,test_corpus
#Get shape of corpus from the dataframe
shape_of_df = wiki_corpus_binary.get_utterances_dataframe().shape
shape_of_df
(2178, 12)
train_corpus,test_corpus = split_dataset(wiki_corpus_binary, 0.90, shape_of_df[0])
print("Shape of training corpus ", train_corpus.get_utterances_dataframe().shape[0])
print("Shape of training corpus ", test_corpus.get_utterances_dataframe().shape[0])
Shape of training corpus 1960 Shape of training corpus 218
train_corpus.get_utterances_dataframe()[0:3]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 214411 | NOT_RECORDED | |style="vertical-align: middle; padding: 3px;"... | user | None | 214411 | 0.619247 | 1 | {'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A... | [{'rt': 20, 'toks': [{'tok': '|style="vertical... | {'feature_politeness_==Please==': 1, 'feature_... | {'politeness_markers_==Please==': [[('please',... | [] |
convokit_clf.fit(train_corpus)
<convokit.classifier.classifier.Classifier at 0x1859e5563c8>
# Predict on test corpus and get prediction score
predict_convokit = convokit_clf.transform(test_corpus)
convokit_clf.transform(wiki_corpus_binary)
<convokit.model.corpus.Corpus at 0x1859c2d1588>
wiki_corpus_binary.get_utterances_dataframe()[0:3]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | meta.prediction | meta.pred_score | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | 0 | 0.153666 | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | 1 | 0.989169 | [] |
| 214411 | NOT_RECORDED | |style="vertical-align: middle; padding: 3px;"... | user | None | 214411 | 0.619247 | 1 | {'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A... | [{'rt': 20, 'toks': [{'tok': '|style="vertical... | {'feature_politeness_==Please==': 1, 'feature_... | {'politeness_markers_==Please==': [[('please',... | 1 | 0.870336 | [] |
prediction_report = convokit_clf.summarize(predict_convokit)
prediction_report
| prediction | pred_score | |
|---|---|---|
| id | ||
| 622173 | 0 | 0.032947 |
| 357406 | 0 | 0.042074 |
| 452664 | 0 | 0.050474 |
| 626728 | 0 | 0.054090 |
| 621114 | 0 | 0.064721 |
| ... | ... | ... |
| 628941 | 1 | 0.994737 |
| 213945 | 1 | 0.996444 |
| 464868 | 1 | 0.996929 |
| 179380 | 1 | 0.998558 |
| 358525 | 1 | 0.998575 |
218 rows × 2 columns
print("--------------------------------------------- Classification Report-------------------------------------------\n")
print(convokit_clf.classification_report(test_corpus))
--------------------------------------------- Classification Report-------------------------------------------
precision recall f1-score support
False 0.72 0.78 0.75 106
True 0.78 0.71 0.74 112
accuracy 0.75 218
macro avg 0.75 0.75 0.75 218
weighted avg 0.75 0.75 0.75 218
print("-------------------------------------------- Confusion Matrix ------------------------------------")
print(convokit_clf.confusion_matrix(test_corpus))
-------------------------------------------- Confusion Matrix ------------------------------------ [[83 23] [32 80]]
# Print confusion_matrix using heat map
ax = sns.heatmap(convokit_clf.confusion_matrix(test_corpus), annot = True)
ax.set_title("Confusion Matrix for Convokit Classifier")
ax.set_xlabel("Actual")
ax.set_ylabel("Predicted ")
plt.show()
utterance_ids = wiki_corpus_binary.get_utterance_ids()
rows = []
for uid in utterance_ids:
rows.append(wiki_corpus_binary.get_utterance(uid).meta["politeness_strategies"])
politeness_strategies = pd.DataFrame(rows, index=utterance_ids)
politeness_strategies
| feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==HASHEDGE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Hedges== | feature_politeness_==Factuality== | feature_politeness_==Deference== | feature_politeness_==Gratitude== | feature_politeness_==Apologizing== | feature_politeness_==1st_person_pl.== | ... | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==HASPOSITIVE== | feature_politeness_==HASNEGATIVE== | feature_politeness_==SUBJUNCTIVE== | feature_politeness_==INDICATIVE== | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 244336 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 214411 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 177439 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 341534 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| 156734 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 147665 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 234095 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 563032 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 |
2178 rows × 21 columns
convokit_clf.get_coefs(politeness_strategies.columns)
| coef | |
|---|---|
| feat_name | |
| feature_politeness_==Gratitude== | 1.178122 |
| feature_politeness_==Indirect_(greeting)== | 0.638553 |
| feature_politeness_==SUBJUNCTIVE== | 0.541239 |
| feature_politeness_==Indirect_(btw)== | 0.467033 |
| feature_politeness_==1st_person_start== | 0.414873 |
| feature_politeness_==Apologizing== | 0.362853 |
| feature_politeness_==HASPOSITIVE== | 0.347574 |
| feature_politeness_==Deference== | 0.309130 |
| feature_politeness_==INDICATIVE== | 0.235271 |
| feature_politeness_==1st_person== | 0.209016 |
| feature_politeness_==Please== | 0.186717 |
| feature_politeness_==HASHEDGE== | 0.126751 |
| feature_politeness_==Hedges== | 0.125405 |
| feature_politeness_==1st_person_pl.== | 0.107767 |
| feature_politeness_==2nd_person== | 0.106503 |
| feature_politeness_==Please_start== | -0.026375 |
| feature_politeness_==2nd_person_start== | -0.123303 |
| feature_politeness_==Direct_start== | -0.275117 |
| feature_politeness_==Direct_question== | -0.279500 |
| feature_politeness_==Factuality== | -0.297774 |
| feature_politeness_==HASNEGATIVE== | -0.419083 |
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import Perceptron
df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()
df_wiki_binary['meta.politeness_strategies']
id
629705 {'feature_politeness_==Please==': 0, 'feature_...
244336 {'feature_politeness_==Please==': 0, 'feature_...
214411 {'feature_politeness_==Please==': 1, 'feature_...
177439 {'feature_politeness_==Please==': 0, 'feature_...
341534 {'feature_politeness_==Please==': 0, 'feature_...
...
60798 {'feature_politeness_==Please==': 0, 'feature_...
156734 {'feature_politeness_==Please==': 0, 'feature_...
147665 {'feature_politeness_==Please==': 0, 'feature_...
234095 {'feature_politeness_==Please==': 0, 'feature_...
563032 {'feature_politeness_==Please==': 0, 'feature_...
Name: meta.politeness_strategies, Length: 2178, dtype: object
To extract these dictionary features, used DictVectorizer provided in sklearn. The DictVectorizer transformer turns mappings into Numpy and perform one hot-encoding for each of the values. For more information refer to sklearn documentation
https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html
from sklearn.feature_extraction import DictVectorizer
dict_vectorizer = DictVectorizer()
dict_vector = dict_vectorizer.fit_transform(df_wiki_binary['meta.politeness_strategies'])
print(dict_vectorizer.get_feature_names())
['feature_politeness_==1st_person==', 'feature_politeness_==1st_person_pl.==', 'feature_politeness_==1st_person_start==', 'feature_politeness_==2nd_person==', 'feature_politeness_==2nd_person_start==', 'feature_politeness_==Apologizing==', 'feature_politeness_==Deference==', 'feature_politeness_==Direct_question==', 'feature_politeness_==Direct_start==', 'feature_politeness_==Factuality==', 'feature_politeness_==Gratitude==', 'feature_politeness_==HASHEDGE==', 'feature_politeness_==HASNEGATIVE==', 'feature_politeness_==HASPOSITIVE==', 'feature_politeness_==Hedges==', 'feature_politeness_==INDICATIVE==', 'feature_politeness_==Indirect_(btw)==', 'feature_politeness_==Indirect_(greeting)==', 'feature_politeness_==Please==', 'feature_politeness_==Please_start==', 'feature_politeness_==SUBJUNCTIVE==']
# Corresponding matrix to dict vectorizer
dict_vector.toarray()
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 1.],
[1., 0., 0., ..., 1., 0., 1.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 1., 0., ..., 0., 0., 1.],
[0., 0., 1., ..., 0., 0., 0.]])
df = pd.DataFrame(dict_vector.toarray(),
index = [x.id for x in wiki_corpus_binary.iter_utterances()],
columns = dict_vectorizer.get_feature_names())
df.columns
Index(['feature_politeness_==1st_person==',
'feature_politeness_==1st_person_pl.==',
'feature_politeness_==1st_person_start==',
'feature_politeness_==2nd_person==',
'feature_politeness_==2nd_person_start==',
'feature_politeness_==Apologizing==',
'feature_politeness_==Deference==',
'feature_politeness_==Direct_question==',
'feature_politeness_==Direct_start==',
'feature_politeness_==Factuality==', 'feature_politeness_==Gratitude==',
'feature_politeness_==HASHEDGE==', 'feature_politeness_==HASNEGATIVE==',
'feature_politeness_==HASPOSITIVE==', 'feature_politeness_==Hedges==',
'feature_politeness_==INDICATIVE==',
'feature_politeness_==Indirect_(btw)==',
'feature_politeness_==Indirect_(greeting)==',
'feature_politeness_==Please==', 'feature_politeness_==Please_start==',
'feature_politeness_==SUBJUNCTIVE=='],
dtype='object')
binary_ = df_wiki_binary['meta.Binary'].astype("category")
change_dicti = { -1 : 0, 1: 1}
binary_.replace(change_dicti , inplace = True )
df_feature = pd.concat([df , binary_], axis = 1)
df_feature
| feature_politeness_==1st_person== | feature_politeness_==1st_person_pl.== | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Apologizing== | feature_politeness_==Deference== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==Factuality== | ... | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 244336 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1 |
| 214411 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1 |
| 177439 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | ... | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 341534 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 156734 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 147665 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 234095 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0 |
| 563032 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
2178 rows × 22 columns
df_feature['meta.Binary'].value_counts()
0 1089 1 1089 Name: meta.Binary, dtype: int64
def convertToDictVector(dictionary_feature, convokit_corpus):
from sklearn.feature_extraction import DictVectorizer
dict_vectorizer = DictVectorizer()
dict_vector = dict_vectorizer.fit_transform(dictionary_feature)
df = pd.DataFrame(dict_vector.toarray(), index = [x.id for x in convokit_corpus.iter_utterances()],
columns = dict_vectorizer.get_feature_names())
binary_feature = convokit_corpus.get_utterances_dataframe()['meta.Binary'].astype("category")
df_feature = pd.concat([df, binary_feature ], axis = 1)
return df_feature
K Cross Validation will be performed to check the performance of our model. For each of the models Kfold will be performed
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
k_fold_cv = KFold(n_splits = 5, random_state = 23, shuffle = True)
To split the data , use train_test_split provided in sklearn.model_selection. Here however, to split the data train_test_split will not be used
def split_data(df_corpus, train_split_percent, shape):
limit = int((train_split_percent)*shape[0])
X_train = df_corpus.iloc[0:limit, 0:(shape[1]-1)]
X_test = df_corpus.iloc[limit:, 0:(shape[1]-1)]
y_train = df_corpus.iloc[0:limit , (shape[1]-1): shape[1]]
y_test = df_corpus.iloc[limit: , (shape[1]-1): shape[1]]
return X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = split_data(df_feature, 0.90, df_feature.shape)
display(X_train[0:3], X_test[0:3], y_train[0:3], y_test[0:3])
| feature_politeness_==1st_person== | feature_politeness_==1st_person_pl.== | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Apologizing== | feature_politeness_==Deference== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==Factuality== | ... | feature_politeness_==HASHEDGE== | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 244336 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 214411 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
3 rows × 21 columns
| feature_politeness_==1st_person== | feature_politeness_==1st_person_pl.== | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Apologizing== | feature_politeness_==Deference== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==Factuality== | ... | feature_politeness_==HASHEDGE== | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 620707 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 75904 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 619003 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
3 rows × 21 columns
| meta.Binary | |
|---|---|
| 629705 | 0 |
| 244336 | 1 |
| 214411 | 1 |
| meta.Binary | |
|---|---|
| 620707 | 1 |
| 75904 | 1 |
| 619003 | 0 |
print("Shape of X_train" , X_train.shape)
print("Shape of X_test" , X_test.shape)
print("Shape of y_train" , y_train.shape)
print("Shape of y_test" , y_test.shape)
Shape of X_train (1960, 21) Shape of X_test (218, 21) Shape of y_train (1960, 1) Shape of y_test (218, 1)
lr = LogisticRegression().fit(X_train,y_train)
To prevent the above warning : use ravel() function to flatten the array. The above warning can be ignored as it will not affect the model
lr_clf = LogisticRegression()
mb_clf = MultinomialNB()
svm_clf_rbf = SVC(kernel = 'rbf')
svm_clf_poly = SVC(kernel = 'poly')
knn_clf = KNeighborsClassifier(n_neighbors = 3 , metric='minkowski')
dtree_clf = DecisionTreeClassifier(criterion ="entropy")
mlp_clf_relu = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,
activation='relu', random_state = 21)
mlp_clf_tanh = MLPClassifier(hidden_layer_sizes = (30,30), max_iter = 1000,
activation = 'tanh', random_state = 22)
precp_clf = Perceptron()
list_class_polite = [lr_clf, mb_clf, svm_clf_rbf, svm_clf_poly, knn_clf, dtree_clf, mlp_clf_relu,
mlp_clf_tanh, precp_clf ]
X_features = df_feature.drop(columns = ['meta.Binary'])
y_response = df_feature['meta.Binary']
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
"MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
]
def crossValidationKFold(model, X_features, y_response, model_name):
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
k_fold_cv = KFold(n_splits = 5, shuffle = True, random_state = 15)
acc_valscore_ = cross_val_score(estimator = model, X = X_features, y = y_response,
cv = k_fold_cv, n_jobs = -1, scoring = "accuracy")
precision_valscore_ = cross_val_score(estimator = model, X = X_features, y = y_response,
cv = k_fold_cv, n_jobs = -1, scoring = "precision")
#get name of the model
print(f"\n******************************** {model_name} Accuracy/Precision Scores *****************************************\n")
print("List of Accuracy Value scores " ,acc_valscore_)
print("\nMean of Accuracy Score ", acc_valscore_.mean())
print("\nMean of Precision Score ", precision_valscore_.mean())
return model_name, acc_valscore_.mean(), acc_valscore_, precision_valscore_.mean()
def KFoldAccuracyScore(list_class_polite, X_features, y_response):
import pandas as pd
mylist = []
count = 0
for model in list_class_polite:
modelname_, acc_valscore_mean, valscore_, precision_valscore_mean = crossValidationKFold(model, X_features, y_response, model_names[count])
mylist.append([modelname_, acc_valscore_mean, precision_valscore_mean])
count+= 1
dataframe = pd.DataFrame(mylist, columns = ['Classifier Names', 'Accuracy Score', 'Precision Score'])
return(dataframe)
accuracy_result = KFoldAccuracyScore(list_class_polite, X_features, y_response)
print("\n\n********************************** K Fold Accuracy and Precision Scores ************************************")
display(accuracy_result)
******************************** Logistic Regression Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.75 0.76834862 0.76834862 0.76781609 0.76551724] Mean of Accuracy Score 0.764006116207951 Mean of Precision Score 0.7829084110131185 ******************************** Naive Bayes Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.77293578 0.70412844 0.73853211 0.76551724 0.74712644] Mean of Accuracy Score 0.7456480016872298 Mean of Precision Score 0.74006242922701 ******************************** Support Vector Machine (Kernel= (RBF)) Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.76376147 0.77752294 0.73394495 0.74712644 0.77701149] Mean of Accuracy Score 0.7598734577665296 Mean of Precision Score 0.7917968342887894 ******************************** Support Vector Machine (Kernel = Polynomial) Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.74311927 0.76834862 0.74311927 0.74942529 0.77701149] Mean of Accuracy Score 0.7562047875144996 Mean of Precision Score 0.8079460708295525 ******************************** K-Nearest Neighbour Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.71100917 0.73165138 0.66513761 0.69425287 0.68505747] Mean of Accuracy Score 0.6974217019930402 Mean of Precision Score 0.7289115387951303 ******************************** Decision Tree Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.72247706 0.74541284 0.68348624 0.71034483 0.71264368] Mean of Accuracy Score 0.7148729305072233 Mean of Precision Score 0.7436079160411841 ******************************** MLP(Multi-Layer Preceptron(Relu)) Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.72477064 0.75458716 0.72247706 0.73563218 0.74252874] Mean of Accuracy Score 0.7359991563851102 Mean of Precision Score 0.7516631590273097 ******************************** MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.72706422 0.74770642 0.70183486 0.71494253 0.73563218] Mean of Accuracy Score 0.7254360434461669 Mean of Precision Score 0.7357896018599673 ******************************** Perceptron Accuracy/Precision Scores ***************************************** List of Accuracy Value scores [0.73165138 0.69495413 0.71559633 0.72183908 0.68045977] Mean of Accuracy Score 0.7089001370874196 Mean of Precision Score 0.6694902560898716 ********************************** K Fold Accuracy and Precision Scores ************************************
| Classifier Names | Accuracy Score | Precision Score | |
|---|---|---|---|
| 0 | Logistic Regression | 0.764006 | 0.782908 |
| 1 | Naive Bayes | 0.745648 | 0.740062 |
| 2 | Support Vector Machine (Kernel= (RBF)) | 0.759873 | 0.791797 |
| 3 | Support Vector Machine (Kernel = Polynomial) | 0.756205 | 0.807946 |
| 4 | K-Nearest Neighbour | 0.697422 | 0.728912 |
| 5 | Decision Tree | 0.714873 | 0.743608 |
| 6 | MLP(Multi-Layer Preceptron(Relu)) | 0.735999 | 0.751663 |
| 7 | MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) | 0.725436 | 0.735790 |
| 8 | Perceptron | 0.708900 | 0.669490 |
lr= lr.fit(X_train,y_train)
lr_clf = lr_clf.fit(X_train,y_train.values.ravel())
mb_clf = mb_clf.fit(X_train,y_train.values.ravel())
svm_clf_rbf = svm_clf_rbf.fit(X_train,y_train.values.ravel())
svm_clf_poly = svm_clf_poly.fit(X_train,y_train.values.ravel())
knn_clf = knn_clf.fit(X_train,y_train.values.ravel())
dtree_clf = dtree_clf.fit(X_train,y_train.values.ravel())
mlp_clf_relu = mlp_clf_relu.fit(X_train,y_train.values.ravel())
mlp_clf_tanh = mlp_clf_tanh.fit(X_train,y_train.values.ravel())
precp_clf = precp_clf.fit(X_train,y_train.values.ravel())
def generateModels(X_train,y_train):
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import Perceptron
lr_clf = LogisticRegression().fit(X_train,y_train.values.ravel())
mb_clf = MultinomialNB().fit(X_train,y_train.values.ravel())
svm_clf_rbf = SVC(kernel = 'rbf').fit(X_train,y_train.values.ravel())
svm_clf_poly = SVC(kernel = 'poly').fit(X_train,y_train.values.ravel())
knn_clf = KNeighborsClassifier(n_neighbors = 3, weights='uniform',
metric='minkowski').fit(X_train,y_train.values.ravel())
dtree_clf = DecisionTreeClassifier(criterion ="entropy").fit(X_train,y_train.values.ravel())
mlp_clf_relu = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,
activation='relu', random_state = 21).fit(X_train,y_train.values.ravel())
mlp_clf_tanh = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,
activation='tanh', random_state = 22).fit(X_train,y_train.values.ravel())
precp_clf = Perceptron().fit(X_train,y_train.values.ravel())
return list([lr_clf, mb_clf, svm_clf_rbf, svm_clf_poly, knn_clf,dtree_clf,
mlp_clf_relu, mlp_clf_tanh, precp_clf])
def performPrediction(classifier, X_test, y_test, color, type_classifier):
from sklearn.metrics import confusion_matrix,classification_report, accuracy_score
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
# Display Confusion Matris
predicted_values = classifier.predict(X_test)
print("\n\n----------------Confusion Matrix for {} Classifier--------------------".format(type_classifier))
clf_matrix = confusion_matrix(y_test , predicted_values)
fig = sns.heatmap(clf_matrix, annot = True, cmap = color, fmt = '.4g')
fig.set_title(f"Seaborn Confusion Matrix for {type_classifier} Classifier\n")
fig.set_xlabel("Predicted Values")
fig.set_ylabel("Actual Values")
fig.xaxis.set_ticklabels(["False","True"])
fig.yaxis.set_ticklabels(["False","True"])
plt.show()
# Display Classification Report
print(f"\n\n\tClassification Report for {type_classifier} Classifier\n")
print(classification_report(y_test, predicted_values, digits = 3))
#Display ROC-AUC Curve
accuracy = accuracy_score(y_test,predicted_values )
fpr, tpr, _ = metrics.roc_curve(y_test, predicted_values)
auc = metrics.roc_auc_score(y_test, predicted_values)
print()
#create ROC curve
plt.plot(fpr,tpr,label=f"AUC for {type_classifier} ="+str(auc))
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.legend(loc=4)
plt.title(f'AUC-ROC plot for {type_classifier} ')
plt.show()
return predicted_values
predicted_values_lr = performPrediction(lr_clf,X_test, y_test,"Blues","Logistic Regression ")
print('\n\n')
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.711 0.764 0.736 106
1 0.760 0.705 0.731 112
accuracy 0.734 218
macro avg 0.735 0.735 0.734 218
weighted avg 0.736 0.734 0.734 218
predicted_values_mb = performPrediction(mb_clf,X_test, y_test,"Greens", "Naive Bayes")
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.738 0.717 0.727 106
1 0.739 0.759 0.749 112
accuracy 0.739 218
macro avg 0.738 0.738 0.738 218
weighted avg 0.739 0.739 0.738 218
predicted_svm_clf_rbf = performPrediction(svm_clf_rbf,X_test, y_test, "Greys", "Support Vector Machine (Kernel= (RBF))")
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.711 0.811 0.758 106
1 0.794 0.688 0.737 112
accuracy 0.748 218
macro avg 0.752 0.749 0.747 218
weighted avg 0.753 0.748 0.747 218
predicted_svm_clf_poly = performPrediction(svm_clf_poly,X_test, y_test, "Oranges", "Support Vector Machine (Kernel = Polynomial)")
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.704 0.830 0.762 106
1 0.806 0.670 0.732 112
accuracy 0.748 218
macro avg 0.755 0.750 0.747 218
weighted avg 0.757 0.748 0.746 218
predicted_knn_clf = performPrediction(knn_clf,X_test, y_test, "Blues" ,"K-Nearest Neighbour")
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.635 0.755 0.690 106
1 0.717 0.589 0.647 112
accuracy 0.670 218
macro avg 0.676 0.672 0.668 218
weighted avg 0.677 0.670 0.668 218
predicted_precp_clf = performPrediction(precp_clf, X_test, y_test,"BuPu","Perceptron")
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.906 0.274 0.420 106
1 0.586 0.973 0.732 112
accuracy 0.633 218
macro avg 0.746 0.623 0.576 218
weighted avg 0.742 0.633 0.580 218
predicted_dtree_clf = performPrediction(dtree_clf,X_test, y_test, "Paired", "Decision Tree")
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.693 0.745 0.718 106
1 0.740 0.688 0.713 112
accuracy 0.716 218
macro avg 0.717 0.716 0.716 218
weighted avg 0.717 0.716 0.716 218
predicted_mlp_clf_relu = performPrediction(mlp_clf_relu, X_test, y_test,"Greens", "MLP(Multi-Layer Preceptron(Relu))")
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.758 0.708 0.732 106
1 0.739 0.786 0.762 112
accuracy 0.748 218
macro avg 0.749 0.747 0.747 218
weighted avg 0.748 0.748 0.747 218
predicted_mlp_clf_tanh = performPrediction(mlp_clf_tanh, X_test, y_test,"Purples", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))")
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.755 0.726 0.740 106
1 0.750 0.777 0.763 112
accuracy 0.752 218
macro avg 0.752 0.752 0.752 218
weighted avg 0.752 0.752 0.752 218
sample_text = "Hi, please let me know if you would proofread my article. It will be great if you could help"
spacy_nlp = spacy.load('en_core_web_sm')
utterance = ps.transform_utterance(sample_text)
data = utterance.meta['politeness_strategies']
dict_vect = DictVectorizer()
dict_vect = dict_vect.fit_transform(data)
dict_vect
<1x21 sparse matrix of type '<class 'numpy.float64'>' with 21 stored elements in Compressed Sparse Row format>
#predicts text as polite
print(lr_clf.predict(dict_vect.toarray()))
[1]
stack_corpus = Corpus(filename=download("stack-exchange-politeness-corpus"))
Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus
# Train classifier on only those records which have reponse -1 or 1
# iter_utterances() -> Iterates over the utterances in the corpus
list_filtered = []
for utterances in stack_corpus.iter_utterances():
if(utterances.meta["Binary"]!= 0):
list_filtered.append(utterances)
stack_corpus = Corpus(utterances = list_filtered)
stack_corpus.get_utterances_dataframe()[10:20]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 0 | NOT_RECORDED | Can you explain more in detail, what should I ... | user | None | 0 | 0.217326 | 0 | {'A33SMNMTMIOJ6T': 12, 'A2OXXHGAM7B0Y': 16, 'A... | [{'rt': 2, 'toks': [{'tok': 'Can', 'tag': 'MD'... | NaN | NaN | [] |
| 1 | NOT_RECORDED | Will expressions always be unambiguously paren... | user | None | 1 | 0.063302 | 0 | {'A1UIH2IMG9DV95': 13, 'A23FB7HE970AZJ': 13, '... | [{'rt': 13, 'toks': [{'tok': 'Will', 'tag': 'M... | NaN | NaN | [] |
| 2 | NOT_RECORDED | how are you resolving function pointers? I am ... | user | None | 2 | 0.128902 | 0 | {'A1BS64O3JY0YJ4': 13, 'A2AE4MZVUX9JPX': 15, '... | [{'rt': 3, 'toks': [{'tok': 'how', 'tag': 'WRB... | NaN | NaN | [] |
| 3 | NOT_RECORDED | What is the definition of `buffer`? Is it a lo... | user | None | 3 | 0.240188 | 0 | {'A3VJDU2VRMN05L': 13, 'A1ZHP80O13CEUI': 13, '... | [{'rt': 1, 'toks': [{'tok': 'What', 'tag': 'WP... | NaN | NaN | [] |
| 4 | NOT_RECORDED | Is `A` a global variable? What is x? | user | None | 4 | 0.508284 | 1 | {'A3OW54MEVDKXJL': 17, 'A2RDZ580VXUO1X': 18, '... | [{'rt': 0, 'toks': [{'tok': 'is', 'tag': 'VBZ'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6598 | NOT_RECORDED | hello. I do not know if others, but to me your... | user | None | 6598 | 0.088219 | 0 | {'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 13, '... | [{'rt': 0, 'toks': [{'tok': 'hello', 'tag': 'U... | NaN | NaN | [] |
| 6599 | NOT_RECORDED | That sounds amazing!! Can I have the link? :D | user | None | 6599 | 0.449894 | 1 | {'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 17, '... | [{'rt': 1, 'toks': [{'tok': 'that', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| 6600 | NOT_RECORDED | Is it easy to use the small pump? Have you not... | user | None | 6600 | -0.268236 | 0 | {'A233ONYNWKDIYF': 13, 'A3S5L3I8O3Q2G': 13, 'A... | [{'rt': 0, 'toks': [{'tok': 'Is', 'tag': 'VBZ'... | NaN | NaN | [] |
| 6601 | NOT_RECORDED | Cool idea. I'm curious, though, how can one dr... | user | None | 6601 | 0.526613 | 1 | {'A1E0EK09CA5OIO': 19, 'A233ONYNWKDIYF': 13, '... | [{'rt': 1, 'toks': [{'tok': 'cool', 'tag': 'UH... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| 6602 | NOT_RECORDED | I've never even questioned this before, but no... | user | None | 6602 | -0.159096 | 0 | {'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 9, 'A... | [{'rt': 4, 'toks': [{'tok': 'I', 'tag': 'PRP',... | NaN | NaN | [] |
6603 rows × 12 columns
stack_text_parser = TextParser(verbosity = 1000)
# Transformer will annotate data with 21 politeness markers
stack_polite_parser = PolitenessStrategies()
stack_corpus = stack_text_parser.transform(stack_corpus)
1000/3302 utterances processed 2000/3302 utterances processed 3000/3302 utterances processed 3302/3302 utterances processed
stack_corpus = stack_polite_parser.transform(stack_corpus, markers = True)
# display utterance in a dataframe
df_stack_corpus = stack_corpus.get_utterances_dataframe()
df_stack_corpus[0:4]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 0 | NOT_RECORDED | Can you explain more in detail, what should I ... | user | None | 0 | 0.217326 | 0 | {'A33SMNMTMIOJ6T': 12, 'A2OXXHGAM7B0Y': 16, 'A... | [{'rt': 2, 'toks': [{'tok': 'Can', 'tag': 'MD'... | NaN | NaN | [] |
| 1 | NOT_RECORDED | Will expressions always be unambiguously paren... | user | None | 1 | 0.063302 | 0 | {'A1UIH2IMG9DV95': 13, 'A23FB7HE970AZJ': 13, '... | [{'rt': 13, 'toks': [{'tok': 'Will', 'tag': 'M... | NaN | NaN | [] |
| 2 | NOT_RECORDED | how are you resolving function pointers? I am ... | user | None | 2 | 0.128902 | 0 | {'A1BS64O3JY0YJ4': 13, 'A2AE4MZVUX9JPX': 15, '... | [{'rt': 3, 'toks': [{'tok': 'how', 'tag': 'WRB... | NaN | NaN | [] |
| 3 | NOT_RECORDED | What is the definition of `buffer`? Is it a lo... | user | None | 3 | 0.240188 | 0 | {'A3VJDU2VRMN05L': 13, 'A1ZHP80O13CEUI': 13, '... | [{'rt': 1, 'toks': [{'tok': 'What', 'tag': 'WP... | NaN | NaN | [] |
| 4 | NOT_RECORDED | Is `A` a global variable? What is x? | user | None | 4 | 0.508284 | 1 | {'A3OW54MEVDKXJL': 17, 'A2RDZ580VXUO1X': 18, '... | [{'rt': 0, 'toks': [{'tok': 'is', 'tag': 'VBZ'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6598 | NOT_RECORDED | hello. I do not know if others, but to me your... | user | None | 6598 | 0.088219 | 0 | {'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 13, '... | [{'rt': 0, 'toks': [{'tok': 'hello', 'tag': 'U... | NaN | NaN | [] |
| 6599 | NOT_RECORDED | That sounds amazing!! Can I have the link? :D | user | None | 6599 | 0.449894 | 1 | {'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 17, '... | [{'rt': 1, 'toks': [{'tok': 'that', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| 6600 | NOT_RECORDED | Is it easy to use the small pump? Have you not... | user | None | 6600 | -0.268236 | 0 | {'A233ONYNWKDIYF': 13, 'A3S5L3I8O3Q2G': 13, 'A... | [{'rt': 0, 'toks': [{'tok': 'Is', 'tag': 'VBZ'... | NaN | NaN | [] |
| 6601 | NOT_RECORDED | Cool idea. I'm curious, though, how can one dr... | user | None | 6601 | 0.526613 | 1 | {'A1E0EK09CA5OIO': 19, 'A233ONYNWKDIYF': 13, '... | [{'rt': 1, 'toks': [{'tok': 'cool', 'tag': 'UH... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| 6602 | NOT_RECORDED | I've never even questioned this before, but no... | user | None | 6602 | -0.159096 | 0 | {'A1E0EK09CA5OIO': 17, 'A233ONYNWKDIYF': 9, 'A... | [{'rt': 4, 'toks': [{'tok': 'I', 'tag': 'PRP',... | NaN | NaN | [] |
6603 rows × 12 columns
# Using convertToDictVector() defined above, again to extract dictionary features
df_standford_features = convertToDictVector(df_stack_corpus['meta.politeness_strategies'], stack_corpus)
df_standford_features['meta.Binary'].replace(change_dicti, inplace = True)
df_standford_features
| feature_politeness_==1st_person== | feature_politeness_==1st_person_pl.== | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Apologizing== | feature_politeness_==Deference== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==Factuality== | ... | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 5 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 9 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 11 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6595 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1 |
| 6596 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6597 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6599 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6601 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
3302 rows × 22 columns
stack_corpus.get_utterances_dataframe().shape
(3302, 12)
X_train, X_test, y_train, y_test = split_data(df_standford_features, 0.90, df_standford_features.shape)
# generate models and store the objects in a list
model_list_standford = generateModels(X_train,y_train)
model_list_standford
[LogisticRegression(),
MultinomialNB(),
SVC(),
SVC(kernel='poly'),
KNeighborsClassifier(n_neighbors=3),
DecisionTreeClassifier(criterion='entropy'),
MLPClassifier(hidden_layer_sizes=(30, 30), max_iter=1000, random_state=21),
MLPClassifier(activation='tanh', hidden_layer_sizes=(30, 30), max_iter=1000,
random_state=22),
Perceptron()]
# Following code will iterate over the list and generate classification report, confusion matrix, ROC curve
# for all models.
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
"Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
"Perceptron"
]
count = 0
for model in model_list_standford:
performPrediction(model, X_test, y_test, "ocean", model_names[count])
count += 1
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.571 0.560 0.565 159
1 0.600 0.610 0.605 172
accuracy 0.586 331
macro avg 0.585 0.585 0.585 331
weighted avg 0.586 0.586 0.586 331
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.558 0.572 0.565 159
1 0.595 0.581 0.588 172
accuracy 0.577 331
macro avg 0.577 0.577 0.577 331
weighted avg 0.577 0.577 0.577 331
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.582 0.516 0.547 159
1 0.595 0.657 0.624 172
accuracy 0.589 331
macro avg 0.588 0.586 0.585 331
weighted avg 0.588 0.589 0.587 331
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.547 0.547 0.547 159
1 0.581 0.581 0.581 172
accuracy 0.565 331
macro avg 0.564 0.564 0.564 331
weighted avg 0.565 0.565 0.565 331
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.571 0.610 0.590 159
1 0.615 0.576 0.595 172
accuracy 0.592 331
macro avg 0.593 0.593 0.592 331
weighted avg 0.594 0.592 0.592 331
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.534 0.585 0.559 159
1 0.580 0.529 0.553 172
accuracy 0.556 331
macro avg 0.557 0.557 0.556 331
weighted avg 0.558 0.556 0.556 331
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.565 0.597 0.581 159
1 0.607 0.576 0.591 172
accuracy 0.586 331
macro avg 0.586 0.587 0.586 331
weighted avg 0.587 0.586 0.586 331
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.560 0.560 0.560 159
1 0.593 0.593 0.593 172
accuracy 0.577 331
macro avg 0.576 0.576 0.576 331
weighted avg 0.577 0.577 0.577 331
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.583 0.044 0.082 159
1 0.524 0.971 0.680 172
accuracy 0.526 331
macro avg 0.553 0.507 0.381 331
weighted avg 0.552 0.526 0.393 331
stack_corpus.get_utterances_dataframe().shape
(3302, 12)
X_train.shape
(2971, 21)
X_test.shape
(331, 21)
X_train_2, X_test_2, y_train_2, y_test_2 = split_data(df_standford_features, 0.95, df_standford_features.shape)
model_list_standford_2 = generateModels(X_train_2 , y_train_2)
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
"Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
"Perceptron"
]
count = 0
for model in model_list_standford_2:
performPrediction(model, X_test_2, y_test_2, "ocean", model_names[count])
count += 1
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.787 0.565 0.658 85
1 0.648 0.840 0.731 81
accuracy 0.699 166
macro avg 0.717 0.702 0.694 166
weighted avg 0.719 0.699 0.693 166
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.776 0.529 0.629 85
1 0.630 0.840 0.720 81
accuracy 0.681 166
macro avg 0.703 0.684 0.674 166
weighted avg 0.705 0.681 0.673 166
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.797 0.553 0.653 85
1 0.645 0.852 0.734 81
accuracy 0.699 166
macro avg 0.721 0.702 0.693 166
weighted avg 0.723 0.699 0.692 166
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.742 0.576 0.649 85
1 0.640 0.790 0.707 81
accuracy 0.681 166
macro avg 0.691 0.683 0.678 166
weighted avg 0.692 0.681 0.677 166
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.725 0.588 0.649 85
1 0.639 0.765 0.697 81
accuracy 0.675 166
macro avg 0.682 0.677 0.673 166
weighted avg 0.683 0.675 0.672 166
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.646 0.600 0.622 85
1 0.609 0.654 0.631 81
accuracy 0.627 166
macro avg 0.627 0.627 0.626 166
weighted avg 0.628 0.627 0.626 166
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.730 0.541 0.622 85
1 0.621 0.790 0.696 81
accuracy 0.663 166
macro avg 0.676 0.666 0.659 166
weighted avg 0.677 0.663 0.658 166
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.701 0.553 0.618 85
1 0.616 0.753 0.678 81
accuracy 0.651 166
macro avg 0.659 0.653 0.648 166
weighted avg 0.660 0.651 0.647 166
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.596 0.765 0.670 85
1 0.649 0.457 0.536 81
accuracy 0.614 166
macro avg 0.623 0.611 0.603 166
weighted avg 0.622 0.614 0.605 166
# build model on all the dataset now instead of splitting it
# df_feature variable is dataset for wikipedia politeness corpus
df_feature.shape
(2178, 22)
X_features = df_feature.drop(columns = ['meta.Binary'])
y_response = df_feature['meta.Binary']
list_class_polite_2 = generateModels(X_features , y_response)
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
"Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
"Perceptron"
]
X_ = df_standford_features.drop(['meta.Binary'], axis = 1)
y_ = df_standford_features['meta.Binary']
count = 0
for model in list_class_polite_2:
performPrediction(model, X_, y_, "Spectral", model_names[count])
count += 1
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.546 0.822 0.656 1651
1 0.639 0.316 0.423 1651
accuracy 0.569 3302
macro avg 0.592 0.569 0.539 3302
weighted avg 0.592 0.569 0.539 3302
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.576 0.741 0.648 1651
1 0.636 0.454 0.530 1651
accuracy 0.597 3302
macro avg 0.606 0.597 0.589 3302
weighted avg 0.606 0.597 0.589 3302
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.551 0.810 0.656 1651
1 0.641 0.339 0.444 1651
accuracy 0.575 3302
macro avg 0.596 0.575 0.550 3302
weighted avg 0.596 0.575 0.550 3302
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.550 0.828 0.661 1651
1 0.652 0.322 0.431 1651
accuracy 0.575 3302
macro avg 0.601 0.575 0.546 3302
weighted avg 0.601 0.575 0.546 3302
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.542 0.772 0.637 1651
1 0.604 0.348 0.441 1651
accuracy 0.560 3302
macro avg 0.573 0.560 0.539 3302
weighted avg 0.573 0.560 0.539 3302
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.546 0.780 0.643 1651
1 0.616 0.353 0.449 1651
accuracy 0.566 3302
macro avg 0.581 0.566 0.546 3302
weighted avg 0.581 0.566 0.546 3302
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.553 0.775 0.646 1651
1 0.624 0.374 0.468 1651
accuracy 0.575 3302
macro avg 0.589 0.575 0.557 3302
weighted avg 0.589 0.575 0.557 3302
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.552 0.731 0.629 1651
1 0.601 0.406 0.485 1651
accuracy 0.568 3302
macro avg 0.577 0.568 0.557 3302
weighted avg 0.577 0.568 0.557 3302
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.531 0.881 0.662 1651
1 0.649 0.221 0.330 1651
accuracy 0.551 3302
macro avg 0.590 0.551 0.496 3302
weighted avg 0.590 0.551 0.496 3302
!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Caution : Recall and Precision is extremely poor for above models (compared to baseline Linguistic Marker model)!!!!!!!!!!!!!!!!!!!!!!!!!!!
wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))
# fiter utterances where binary variables were non zero
wiki_corpus_binary = Corpus(utterances = [utt for utt in wiki_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])
# use Text Parser (parser has been pre loaded at the beginning )
wiki_corpus_binary = parser.transform(wiki_corpus_binary)
# use Politeness Transformer to annotate our data
wiki_corpus_binary = ps.transform(wiki_corpus_binary, markers = True)
df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()
display(df_wiki_binary.head())
Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus 2000/2178 utterances processed 2178/2178 utterances processed
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 214411 | NOT_RECORDED | |style="vertical-align: middle; padding: 3px;"... | user | None | 214411 | 0.619247 | 1 | {'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A... | [{'rt': 20, 'toks': [{'tok': '|style="vertical... | {'feature_politeness_==Please==': 1, 'feature_... | {'politeness_markers_==Please==': [[('please',... | [] |
| 177439 | NOT_RECORDED | These are my numbers: 7 years in Wikipedia, 6 ... | user | None | 177439 | -0.473539 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A26YKYQIA3GX8B': 5, 'A... | [{'rt': 1, 'toks': [{'tok': 'these', 'tag': 'D... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 341534 | NOT_RECORDED | I couldn't tell you why glam rock was there. B... | user | None | 341534 | -0.962907 | -1 | {'A233ONYNWKDIYF': 9, 'A2UFD1I8ZO1V4G': 9, 'A3... | [{'rt': 3, 'toks': [{'tok': 'i', 'tag': 'PRP',... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
stack_corpus = Corpus(download("stack-exchange-politeness-corpus"))
stack_corpus_binary = Corpus(utterances = [utt for utt in stack_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])
stack_corpus_binary = parser.transform(stack_corpus_binary)
stack_corpus_binary = ps.transform(stack_corpus_binary, markers = True)
df_stack_binary = stack_corpus_binary.get_utterances_dataframe()
display(df_stack_binary.head())
Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus 2000/3302 utterances processed 3302/3302 utterances processed
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 4 | NOT_RECORDED | Is `A` a global variable? What is x? | user | None | 4 | 0.508284 | 1 | {'A3OW54MEVDKXJL': 17, 'A2RDZ580VXUO1X': 18, '... | [{'rt': 0, 'toks': [{'tok': 'is', 'tag': 'VBZ'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 5 | NOT_RECORDED | This is a very confusing question! How are yo... | user | None | 5 | -0.393623 | -1 | {'A2WKPCZU4U110T': 16, 'A1BS64O3JY0YJ4': 14, '... | [{'rt': 1, 'toks': [{'tok': 'this', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 6 | NOT_RECORDED | Why not using `isnan()` from math.h? Any speci... | user | None | 6 | -0.689701 | -1 | {'AL97SCCNKZILP': 7, 'A3E157ZN8XPUKJ': 20, 'A2... | [{'rt': 2, 'toks': [{'tok': 'why', 'tag': 'WRB... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 9 | NOT_RECORDED | Does your project involve some graphical user ... | user | None | 9 | 0.519398 | 1 | {'A2UFD1I8ZO1V4G': 17, 'A3MMLCBV2W3BP9': 13, '... | [{'rt': 3, 'toks': [{'tok': 'does', 'tag': 'VB... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 11 | NOT_RECORDED | Usually compilers should generate a good code ... | user | None | 11 | 0.631237 | 1 | {'A2TMSM19YCEXLE': 20, 'A28TXBSZPWMEU9': 15, '... | [{'rt': 3, 'toks': [{'tok': 'usually', 'tag': ... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
from convokit import BoWTransformer
bow_transformer = BoWTransformer(obj_type = "utterance", vector_name = 'bow')
bow_transformer2 = BoWTransformer(obj_type = "utterance", vector_name = 'bow')
Initializing default unigram CountVectorizer...Done. Initializing default unigram CountVectorizer...Done.
bow_transformer.fit_transform(wiki_corpus_binary)
bow_transformer2.fit_transform(stack_corpus_binary)
<convokit.model.corpus.Corpus at 0x185bf752e08>
print(wiki_corpus_binary.vectors)
print(wiki_corpus_binary.vectors)
{'bow'}
{'bow'}
df_wiki_binary[0:2]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
len(bow_transformer.get_vocabulary())
563
rand_utt = wiki_corpus_binary.random_utterance()
display(rand_utt.get_vector('bow', as_dataframe = True))
| able | about | above | account | actually | add | added | adding | address | admin | ... | written | wrong | wrote | yeah | year | years | yes | yet | your | yourself | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 627774 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 rows × 563 columns
BoW Transformer contains the vector representation of the text.
bowvect_ = wiki_corpus_binary.get_vector_matrix('bow').to_dataframe()
bowvect_
| able | about | above | account | actually | add | added | adding | address | admin | ... | written | wrong | wrote | yeah | year | years | yes | yet | your | yourself | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 244336 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 214411 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 177439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | ... | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 |
| 341534 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 156734 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 147665 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 234095 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 563032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2178 rows × 563 columns
binary_ = pd.DataFrame(binary_ )
binary_
| meta.Binary | |
|---|---|
| id | |
| 629705 | 0 |
| 244336 | 1 |
| 214411 | 1 |
| 177439 | 0 |
| 341534 | 0 |
| ... | ... |
| 60798 | 1 |
| 156734 | 1 |
| 147665 | 0 |
| 234095 | 0 |
| 563032 | 0 |
2178 rows × 1 columns
df_bow_features = pd.concat([bowvect_ , binary_], axis = 1)
df_bow_features
| able | about | above | account | actually | add | added | adding | address | admin | ... | wrong | wrote | yeah | year | years | yes | yet | your | yourself | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 244336 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 214411 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 177439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | ... | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| 341534 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 156734 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 147665 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 234095 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 563032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2178 rows × 564 columns
X_trainbow, X_testbow, y_trainbow, y_testbow = split_data(df_bow_features, 0.90, df_bow_features.shape)
X_trainbow
| able | about | above | account | actually | add | added | adding | address | admin | ... | written | wrong | wrote | yeah | year | years | yes | yet | your | yourself | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 244336 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 214411 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 177439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | ... | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 |
| 341534 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 356288 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 511427 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 620601 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 334380 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 620704 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1960 rows × 563 columns
lrbow_ = LogisticRegression().fit(X_trainbow, y_trainbow.values.ravel())
mbbow_ = MultinomialNB().fit(X_trainbow, y_trainbow.values.ravel())
svmbow_rbf = SVC(kernel = 'rbf',).fit(X_trainbow, y_trainbow.values.ravel())
svmbow_poly = SVC(kernel = 'poly').fit(X_trainbow, y_trainbow.values.ravel())
knnbow_ = KNeighborsClassifier(n_neighbors = 3).fit(X_trainbow, y_trainbow.values.ravel())
dtreebow_ = DecisionTreeClassifier(criterion = 'entropy').fit(X_trainbow, y_trainbow.values.ravel())
mlpbow_relu = MLPClassifier(hidden_layer_sizes = (30,30), activation = 'relu', max_iter = 500).fit(X_trainbow , y_trainbow.values.ravel())
mlpbow_tanh = MLPClassifier(hidden_layer_sizes = (30,30), activation = 'tanh', max_iter = 500).fit(X_trainbow , y_trainbow.values.ravel())
precpbow_ = Perceptron().fit(X_trainbow, y_trainbow.values.ravel())
list_bow_classifier = [lrbow_, mbbow_, svmbow_rbf,svmbow_poly, knnbow_, dtreebow_,
mlpbow_relu, mlpbow_tanh, precpbow_ ]
count = 0
for model in list_bow_classifier:
performPrediction(model, X_testbow, y_testbow, "Paired", model_names[count])
count += 1
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.791 0.821 0.806 106
1 0.824 0.795 0.809 112
accuracy 0.807 218
macro avg 0.807 0.808 0.807 218
weighted avg 0.808 0.807 0.807 218
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.792 0.792 0.792 106
1 0.804 0.804 0.804 112
accuracy 0.798 218
macro avg 0.798 0.798 0.798 218
weighted avg 0.798 0.798 0.798 218
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.781 0.840 0.809 106
1 0.837 0.777 0.806 112
accuracy 0.807 218
macro avg 0.809 0.808 0.807 218
weighted avg 0.809 0.807 0.807 218
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.608 0.906 0.727 106
1 0.833 0.446 0.581 112
accuracy 0.670 218
macro avg 0.720 0.676 0.654 218
weighted avg 0.724 0.670 0.652 218
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.511 0.877 0.646 106
1 0.639 0.205 0.311 112
accuracy 0.532 218
macro avg 0.575 0.541 0.478 218
weighted avg 0.577 0.532 0.474 218
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.750 0.764 0.757 106
1 0.773 0.759 0.766 112
accuracy 0.761 218
macro avg 0.761 0.762 0.761 218
weighted avg 0.762 0.761 0.762 218
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.793 0.830 0.811 106
1 0.832 0.795 0.813 112
accuracy 0.812 218
macro avg 0.812 0.812 0.812 218
weighted avg 0.813 0.812 0.812 218
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.744 0.821 0.780 106
1 0.812 0.732 0.770 112
accuracy 0.775 218
macro avg 0.778 0.776 0.775 218
weighted avg 0.779 0.775 0.775 218
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.839 0.736 0.784 106
1 0.776 0.866 0.819 112
accuracy 0.803 218
macro avg 0.807 0.801 0.801 218
weighted avg 0.806 0.803 0.802 218
df_stack_binary[0:3]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 4 | NOT_RECORDED | Is `A` a global variable? What is x? | user | None | 4 | 0.508284 | 1 | {'A3OW54MEVDKXJL': 17, 'A2RDZ580VXUO1X': 18, '... | [{'rt': 0, 'toks': [{'tok': 'is', 'tag': 'VBZ'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| 5 | NOT_RECORDED | This is a very confusing question! How are yo... | user | None | 5 | -0.393623 | -1 | {'A2WKPCZU4U110T': 16, 'A1BS64O3JY0YJ4': 14, '... | [{'rt': 1, 'toks': [{'tok': 'this', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
| 6 | NOT_RECORDED | Why not using `isnan()` from math.h? Any speci... | user | None | 6 | -0.689701 | -1 | {'AL97SCCNKZILP': 7, 'A3E157ZN8XPUKJ': 20, 'A2... | [{'rt': 2, 'toks': [{'tok': 'why', 'tag': 'WRB... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [bow] |
stack_bowvect_ = stack_corpus_binary.get_vector_matrix('bow').to_dataframe()
stack_bowvect_
| 10 | 100 | 30 | _drupal | able | about | above | accept | access | accomplish | ... | writing | written | wrong | www | xml | yeah | yes | yet | your | yourself | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6595 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6596 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6597 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6599 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6601 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3302 rows × 717 columns
len(bow_transformer2.get_vocabulary())
717
df_bow_stack = pd.concat([stack_bowvect_ , df_stack_binary['meta.Binary'].astype("category")], axis = 1)
df_bow_stack[0:5]
| 10 | 100 | 30 | _drupal | able | about | above | accept | access | accomplish | ... | written | wrong | www | xml | yeah | yes | yet | your | yourself | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | -1 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
5 rows × 718 columns
df_bow_stack['meta.Binary'].replace({1:1, -1:0}, inplace = True)
df_bow_stack
| 10 | 100 | 30 | _drupal | able | about | above | accept | access | accomplish | ... | written | wrong | www | xml | yeah | yes | yet | your | yourself | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6595 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6596 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6597 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6599 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6601 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
3302 rows × 718 columns
X_trainbow, X_testbow, y_trainbow, y_testbow = split_data(df_bow_stack, 0.90, df_bow_stack.shape)
list_clf_stack_bow = generateModels(X_trainbow, y_trainbow)
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
"MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
]
count = 0
for model in list_clf_stack_bow:
performPrediction(model, X_testbow, y_testbow, "Spectral", model_names[count])
count += 1
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.581 0.610 0.595 159
1 0.622 0.593 0.607 172
accuracy 0.601 331
macro avg 0.601 0.602 0.601 331
weighted avg 0.602 0.601 0.601 331
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.590 0.616 0.603 159
1 0.630 0.605 0.617 172
accuracy 0.610 331
macro avg 0.610 0.611 0.610 331
weighted avg 0.611 0.610 0.610 331
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.595 0.610 0.602 159
1 0.631 0.616 0.624 172
accuracy 0.613 331
macro avg 0.613 0.613 0.613 331
weighted avg 0.614 0.613 0.613 331
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.515 0.881 0.650 159
1 0.678 0.233 0.346 172
accuracy 0.544 331
macro avg 0.596 0.557 0.498 331
weighted avg 0.600 0.544 0.492 331
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.506 0.824 0.627 159
1 0.611 0.256 0.361 172
accuracy 0.529 331
macro avg 0.558 0.540 0.494 331
weighted avg 0.561 0.529 0.488 331
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.486 0.535 0.509 159
1 0.526 0.477 0.500 172
accuracy 0.505 331
macro avg 0.506 0.506 0.504 331
weighted avg 0.506 0.505 0.504 331
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.542 0.566 0.554 159
1 0.582 0.558 0.570 172
accuracy 0.562 331
macro avg 0.562 0.562 0.562 331
weighted avg 0.563 0.562 0.562 331
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.540 0.553 0.547 159
1 0.577 0.564 0.571 172
accuracy 0.559 331
macro avg 0.559 0.559 0.559 331
weighted avg 0.559 0.559 0.559 331
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.533 0.509 0.521 159
1 0.564 0.587 0.575 172
accuracy 0.550 331
macro avg 0.549 0.548 0.548 331
weighted avg 0.549 0.550 0.549 331
df_bow_features[0:3]
| able | about | above | account | actually | add | added | adding | address | admin | ... | wrong | wrote | yeah | year | years | yes | yet | your | yourself | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 244336 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 214411 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
3 rows × 564 columns
df_feature[0:3]
| feature_politeness_==1st_person== | feature_politeness_==1st_person_pl.== | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Apologizing== | feature_politeness_==Deference== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==Factuality== | ... | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 244336 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1 |
| 214411 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1 |
3 rows × 22 columns
bow_and_pstrat_ = pd.concat([df_bow_features.drop(columns = ['meta.Binary']), df_feature], axis =1)
bow_and_pstrat_
| able | about | above | account | actually | add | added | adding | address | admin | ... | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 244336 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1 |
| 214411 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1 |
| 177439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | ... | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 341534 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 156734 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 147665 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 234095 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0 |
| 563032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
2178 rows × 585 columns
X_train_bow_polite, X_test_bow_polite, y_train_bow_polite, y_test_bow_polite = split_data(bow_and_pstrat_ , 0.90,
bow_and_pstrat_ .shape)
list_bow_polite = generateModels(X_train_bow_polite , y_train_bow_polite)
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
"MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
]
count = 0
for model in list_bow_polite:
performPrediction(model, X_test_bow_polite, y_test_bow_polite, "CMRmap", model_names[count])
count += 1
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.819 0.811 0.815 106
1 0.823 0.830 0.827 112
accuracy 0.821 218
macro avg 0.821 0.821 0.821 218
weighted avg 0.821 0.821 0.821 218
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.808 0.792 0.800 106
1 0.807 0.821 0.814 112
accuracy 0.807 218
macro avg 0.807 0.807 0.807 218
weighted avg 0.807 0.807 0.807 218
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.827 0.811 0.819 106
1 0.825 0.839 0.832 112
accuracy 0.826 218
macro avg 0.826 0.825 0.825 218
weighted avg 0.826 0.826 0.826 218
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.658 0.925 0.769 106
1 0.884 0.545 0.674 112
accuracy 0.729 218
macro avg 0.771 0.735 0.721 218
weighted avg 0.774 0.729 0.720 218
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.582 0.868 0.697 106
1 0.767 0.411 0.535 112
accuracy 0.633 218
macro avg 0.674 0.639 0.616 218
weighted avg 0.677 0.633 0.614 218
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.722 0.736 0.729 106
1 0.745 0.732 0.739 112
accuracy 0.734 218
macro avg 0.734 0.734 0.734 218
weighted avg 0.734 0.734 0.734 218
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.804 0.811 0.808 106
1 0.820 0.812 0.816 112
accuracy 0.812 218
macro avg 0.812 0.812 0.812 218
weighted avg 0.812 0.812 0.812 218
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.757 0.792 0.774 106
1 0.794 0.759 0.776 112
accuracy 0.775 218
macro avg 0.776 0.776 0.775 218
weighted avg 0.776 0.775 0.775 218
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.814 0.745 0.778 106
1 0.777 0.839 0.807 112
accuracy 0.794 218
macro avg 0.796 0.792 0.793 218
weighted avg 0.795 0.794 0.793 218
df_bow_features
| able | about | above | account | actually | add | added | adding | address | admin | ... | wrong | wrote | yeah | year | years | yes | yet | your | yourself | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 244336 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 214411 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 177439 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | ... | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| 341534 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 156734 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 147665 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 234095 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 563032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2178 rows × 564 columns
df_bow_stack
| 10 | 100 | 30 | _drupal | able | about | above | accept | access | accomplish | ... | written | wrong | www | xml | yeah | yes | yet | your | yourself | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6595 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6596 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6597 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6599 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6601 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
3302 rows × 718 columns
df_standford_features
| feature_politeness_==1st_person== | feature_politeness_==1st_person_pl.== | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Apologizing== | feature_politeness_==Deference== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==Factuality== | ... | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 5 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 9 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 11 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6595 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1 |
| 6596 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6597 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6599 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6601 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
3302 rows × 22 columns
bow_and_pstrat_stack = pd.concat([df_bow_stack.drop(columns = ['meta.Binary']), df_standford_features], axis =1)
bow_and_pstrat_stack
| 10 | 100 | 30 | _drupal | able | about | above | accept | access | accomplish | ... | feature_politeness_==HASNEGATIVE== | feature_politeness_==HASPOSITIVE== | feature_politeness_==Hedges== | feature_politeness_==INDICATIVE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==SUBJUNCTIVE== | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6595 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1 |
| 6596 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6597 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6599 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 6601 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
3302 rows × 739 columns
X_train_bow_polite_2, X_test_bow_polite_2, y_train_bow_polite_2, y_test_bow_polite_2 = split_data(bow_and_pstrat_stack, 0.90,
bow_and_pstrat_stack.shape)
list_bow_polite_stack = generateModels(X_train_bow_polite_2 , y_train_bow_polite_2)
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
"MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
]
X_train_bow_polite_2
X_test_bow_polite_2.shape
(331, 738)
count = 0
for model in list_bow_polite_stack:
performPrediction(model, X_test_bow_polite_2, y_test_bow_polite_2, "coolwarm", model_names[count])
count += 1
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.575 0.604 0.589 159
1 0.616 0.587 0.601 172
accuracy 0.595 331
macro avg 0.595 0.595 0.595 331
weighted avg 0.596 0.595 0.595 331
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.581 0.629 0.604 159
1 0.629 0.581 0.604 172
accuracy 0.604 331
macro avg 0.605 0.605 0.604 331
weighted avg 0.606 0.604 0.604 331
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.635 0.635 0.635 159
1 0.663 0.663 0.663 172
accuracy 0.650 331
macro avg 0.649 0.649 0.649 331
weighted avg 0.650 0.650 0.650 331
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.529 0.855 0.654 159
1 0.689 0.297 0.415 172
accuracy 0.565 331
macro avg 0.609 0.576 0.534 331
weighted avg 0.612 0.565 0.530 331
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.498 0.786 0.610 159
1 0.575 0.267 0.365 172
accuracy 0.517 331
macro avg 0.537 0.527 0.487 331
weighted avg 0.538 0.517 0.483 331
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.552 0.572 0.562 159
1 0.590 0.570 0.580 172
accuracy 0.571 331
macro avg 0.571 0.571 0.571 331
weighted avg 0.572 0.571 0.571 331
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.572 0.597 0.585 159
1 0.612 0.587 0.599 172
accuracy 0.592 331
macro avg 0.592 0.592 0.592 331
weighted avg 0.593 0.592 0.592 331
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.531 0.491 0.510 159
1 0.560 0.599 0.579 172
accuracy 0.547 331
macro avg 0.545 0.545 0.544 331
weighted avg 0.546 0.547 0.546 331
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.548 0.679 0.607 159
1 0.619 0.483 0.542 172
accuracy 0.577 331
macro avg 0.584 0.581 0.575 331
weighted avg 0.585 0.577 0.573 331
Term frequency refers to the frequency of a term t in document d. The inverse document frequency is a measure of whether a term is common or rare. To analyze utterance text, convert text to its correspondinf tf-idf vector
from convokit import ColNormedTfidfTransformer
# lst= []
# id_list = []
# for utt in wiki_corpus_binary.iter_utterances():
# lst.append(utt.text.lower())
# lst
# from sklearn.feature_extraction.text import TfidfTransformer
# from sklearn.feature_extraction.text import CountVectorizer
# from sklearn.pipeline import Pipeline
# vectorizer = CountVectorizer(stop_words='english',analyzer='word')
# document_term_matrix = vectorizer.fit_transform(lst)
# document_term_matrix.shape
# tfidf_transformer = TfidfTransformer()
# tf_idf = tfidf_transformer.fit_transform(document_term_matrix )
# kl = list(vectorizer.vocabulary_.keys())
# len(kl)
# df_feature
# x = pd.DataFrame(tf_idf.toarray() ,columns = kl )
# x
# print(list(x.columns))
def load_wiki_politeness():
''' Function loads the corpus '''
from convokit import TextParser, PolitenessStrategies
parser = TextParser(verbosity = 2000)
ps = PolitenessStrategies()
wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))
wiki_corpus_binary = Corpus(utterances = [utt for utt in wiki_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])
wiki_corpus_binary = parser.transform(wiki_corpus_binary)
wiki_corpus_binary = ps.transform(wiki_corpus_binary, markers = True)
df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()
df_wiki_binary
return wiki_corpus_binary, df_wiki_binary
#reload the corpus
wiki_corpus_binary,df_wiki_binary = load_wiki_politeness()
Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus 2000/2178 utterances processed 2178/2178 utterances processed
df_wiki_binary[0:3]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 214411 | NOT_RECORDED | |style="vertical-align: middle; padding: 3px;"... | user | None | 214411 | 0.619247 | 1 | {'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A... | [{'rt': 20, 'toks': [{'tok': '|style="vertical... | {'feature_politeness_==Please==': 1, 'feature_... | {'politeness_markers_==Please==': [[('please',... | [] |
parser = TextParser(verbosity = 2000)
ps = PolitenessStrategies()
wiki_corpus = Corpus(download('wikipedia-politeness-corpus'))
wiki_corpus_binary = Corpus(utterances = [utt for utt in wiki_corpus.iter_utterances() if utt.meta['Binary']!= 0 ])
wiki_corpus_binary = parser.transform(wiki_corpus_binary)
wiki_corpus_binary = ps.transform(wiki_corpus_binary, markers = True)
df_wiki_binary = wiki_corpus_binary.get_utterances_dataframe()
df_wiki_binary.shape
Dataset already exists at C:\Users\pulki\capstone project\march 11th 2021 politeness\wikipedia-politeness-corpus 2000/2178 utterances processed 2178/2178 utterances processed
(2178, 12)
from convokit.expected_context_framework import ColNormedTfidfTransformer
td_idf = ColNormedTfidfTransformer(input_field = 'text', output_field='col_normed_tfidf', model=None )
td_idf.fit_transform(wiki_corpus_binary)
<convokit.model.corpus.Corpus at 0x185994f2cc8>
wiki_corpus_binary.get_utterances_dataframe()[0:6]
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | meta.col_normed_tfidf__n_feats | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 629705 | NOT_RECORDED | Where did you learn English? How come you're t... | user | None | 629705 | -1.120049 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A2YFPO0N4GIS25': 9, 'A... | [{'rt': 3, 'toks': [{'tok': 'where', 'tag': 'W... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | 13 | [col_normed_tfidf] |
| 244336 | NOT_RECORDED | Thanks very much for your edit to the <url> ar... | user | None | 244336 | 1.313955 | 1 | {'A2QN0EGBRGJU1M': 23, 'A2GSW5RBAT5LQ5': 16, '... | [{'rt': 0, 'toks': [{'tok': 'thanks', 'tag': '... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | 18 | [col_normed_tfidf] |
| 214411 | NOT_RECORDED | |style="vertical-align: middle; padding: 3px;"... | user | None | 214411 | 0.619247 | 1 | {'AYG3MF094634L': 14, 'A1F4D2PZ7NNWTL': 16, 'A... | [{'rt': 20, 'toks': [{'tok': '|style="vertical... | {'feature_politeness_==Please==': 1, 'feature_... | {'politeness_markers_==Please==': [[('please',... | 26 | [col_normed_tfidf] |
| 177439 | NOT_RECORDED | These are my numbers: 7 years in Wikipedia, 6 ... | user | None | 177439 | -0.473539 | -1 | {'A2UFD1I8ZO1V4G': 13, 'A26YKYQIA3GX8B': 5, 'A... | [{'rt': 1, 'toks': [{'tok': 'these', 'tag': 'D... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | 57 | [col_normed_tfidf] |
| 341534 | NOT_RECORDED | I couldn't tell you why glam rock was there. B... | user | None | 341534 | -0.962907 | -1 | {'A233ONYNWKDIYF': 9, 'A2UFD1I8ZO1V4G': 9, 'A3... | [{'rt': 3, 'toks': [{'tok': 'i', 'tag': 'PRP',... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | 10 | [col_normed_tfidf] |
| 567951 | NOT_RECORDED | Are you calling me a vandal for visiting your ... | user | None | 567951 | -0.816661 | -1 | {'A233ONYNWKDIYF': 9, 'A2WKPCZU4U110T': 10, 'A... | [{'rt': 2, 'toks': [{'tok': 'are', 'tag': 'VBP... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | 16 | [col_normed_tfidf] |
#display tf-idf vector
rand_ = wiki_corpus_binary.random_utterance()
display(rand_.get_vector('col_normed_tfidf', as_dataframe = True))
| ! | !vote | !voter | " | "#redirect" | "'''bong'''warrior"? | "''<url>'' | "''images | "(this | "...definitions...". | ... | zhongda | zim | zionist | zooms | zuu | | | |2 | |30em | |style="vertical-align: | |} | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 188936 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
1 rows × 9438 columns
wiki_corpus_binary.vectors
{'col_normed_tfidf'}
# display tf-idf matrix
td_idf_vector = wiki_corpus_binary.get_vector_matrix('col_normed_tfidf').to_dataframe()
td_idf_vector
| ! | !vote | !voter | " | "#redirect" | "'''bong'''warrior"? | "''<url>'' | "''images | "(this | "...definitions...". | ... | zhongda | zim | zionist | zooms | zuu | | | |2 | |30em | |style="vertical-align: | |} | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 244336 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 214411 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.821102 | 0.0 | 0.0 | 0.821102 | 0.0 |
| 177439 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 341534 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 156734 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 147665 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 234095 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 563032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 |
2178 rows × 9438 columns
# combine features (vocabulary) and outcome variable
pd.concat([td_idf_vector,binary_],axis=1)
| ! | !vote | !voter | " | "#redirect" | "'''bong'''warrior"? | "''<url>'' | "''images | "(this | "...definitions...". | ... | zim | zionist | zooms | zuu | | | |2 | |30em | |style="vertical-align: | |} | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 629705 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0 |
| 244336 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 1 |
| 214411 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.821102 | 0.0 | 0.0 | 0.821102 | 0.0 | 1 |
| 177439 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0 |
| 341534 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 60798 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 1 |
| 156734 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 1 |
| 147665 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0 |
| 234095 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0 |
| 563032 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0 |
2178 rows × 9439 columns
tf_idf_vect_clf = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_mb = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf', clf = MultinomialNB(),
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_dt = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
clf = DecisionTreeClassifier(criterion = 'entropy'),
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_knn = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
clf = KNeighborsClassifier(n_neighbors = 3),
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_svc_rbf = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
clf = SVC(kernel = 'rbf'),
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_svc_poly = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
clf = SVC(kernel = 'poly'),
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_mlp_relu = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
clf = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, activation='relu', random_state = 21),
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_mlp_tanh = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
clf = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000, activation='tanh', random_state = 22),
labeller=lambda utt: utt.meta['Binary'] == 1)
tf_idf_vect_clf_precp = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
clf = Perceptron(),
labeller=lambda utt: utt.meta['Binary'] == 1)
Initialized default classification model (standard scaled logistic regression).
scores_lr = tf_idf_vect_clf.evaluate_with_cv(wiki_corpus_binary)
print(scores_lr)
Running a cross-validated evaluation...Done. [0.74770642 0.72477064 0.76605505 0.75172414 0.74482759]
scores_mb = tf_idf_vect_clf_mb.evaluate_with_cv(wiki_corpus_binary)
print(scores_mb)
Running a cross-validated evaluation...Done. [0.68577982 0.70183486 0.69954128 0.70804598 0.71724138]
scores_dt = tf_idf_vect_clf_dt.evaluate_with_cv(wiki_corpus_binary)
Running a cross-validated evaluation...Done.
scores_knn = tf_idf_vect_clf_knn.evaluate_with_cv(wiki_corpus_binary)
Running a cross-validated evaluation...Done.
scores_svc_1 = tf_idf_vect_clf_svc_rbf.evaluate_with_cv(wiki_corpus_binary)
Running a cross-validated evaluation...Done.
scores_svc_2 = tf_idf_vect_clf_svc_poly.evaluate_with_cv(wiki_corpus_binary)
print(scores_svc_2)
scores_mlp_1 = tf_idf_vect_clf_mlp_relu.evaluate_with_cv(wiki_corpus_binary)
scores_mlp_2 = tf_idf_vect_clf_mlp_tanh.evaluate_with_cv(wiki_corpus_binary)
scores_precp = tf_idf_vect_clf_precp.evaluate_with_cv(wiki_corpus_binary)
Running a cross-validated evaluation...Done. [0.53211009 0.50229358 0.46330275 0.46896552 0.53563218] Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done.
score_ = [np.mean(scores_lr), np.mean(scores_mb), np.mean(scores_dt),
np.mean(scores_knn),np.mean(scores_svc_1),np.mean(scores_svc_2),
np.mean(scores_mlp_1),np.mean(scores_mlp_2),np.mean(scores_precp)]
frame = pd.DataFrame({ 'Cross Validation Mean score ': score_}, index = ['Standard Logistic Regression',
'Mutinomial Naive Bayes', 'Decision Tree',
'KNeighbors Classifiers','SVC RBF','SVC Poly',
'MLP (relu)','MLP(tanh)','Perceptron '])
frame
| Cross Validation Mean score | |
|---|---|
| Standard Logistic Regression | 0.747017 |
| Mutinomial Naive Bayes | 0.702489 |
| Decision Tree | 0.708896 |
| KNeighbors Classifiers | 0.511028 |
| SVC RBF | 0.654288 |
| SVC Poly | 0.500461 |
| MLP (relu) | 0.742422 |
| MLP(tanh) | 0.732797 |
| Perceptron | 0.722659 |
X_train_tf, X_test_tf, y_train_tf, y_test_tf = split_data(pd.concat([td_idf_vector,binary_],axis=1), 0.90, pd.concat([td_idf_vector,binary_],axis=1).shape)
#Generate classifiers and train them using training data
tf_idf_models = generateModels(X_train_tf,y_train_tf)
model_names = ["Logisitc Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
"MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"
]
count = 0
for model in tf_idf_models:
performPrediction(model, X_test_tf, y_test_tf, "Paired", model_names[count])
count += 1
----------------Confusion Matrix for Logisitc Regression Classifier--------------------
Classification Report for Logisitc Regression Classifier
precision recall f1-score support
0 0.780 0.868 0.821 106
1 0.860 0.768 0.811 112
accuracy 0.817 218
macro avg 0.820 0.818 0.816 218
weighted avg 0.821 0.817 0.816 218
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.808 0.594 0.685 106
1 0.693 0.866 0.770 112
accuracy 0.734 218
macro avg 0.750 0.730 0.727 218
weighted avg 0.749 0.734 0.728 218
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.742 0.679 0.709 106
1 0.719 0.777 0.747 112
accuracy 0.729 218
macro avg 0.731 0.728 0.728 218
weighted avg 0.730 0.729 0.729 218
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.000 0.000 0.000 106
1 0.514 1.000 0.679 112
accuracy 0.514 218
macro avg 0.257 0.500 0.339 218
weighted avg 0.264 0.514 0.349 218
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 1.000 0.038 0.073 106
1 0.523 1.000 0.687 112
accuracy 0.532 218
macro avg 0.762 0.519 0.380 218
weighted avg 0.755 0.532 0.388 218
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.694 0.726 0.710 106
1 0.729 0.696 0.712 112
accuracy 0.711 218
macro avg 0.711 0.711 0.711 218
weighted avg 0.712 0.711 0.711 218
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.756 0.849 0.800 106
1 0.838 0.741 0.787 112
accuracy 0.794 218
macro avg 0.797 0.795 0.793 218
weighted avg 0.798 0.794 0.793 218
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.763 0.849 0.804 106
1 0.840 0.750 0.792 112
accuracy 0.798 218
macro avg 0.801 0.800 0.798 218
weighted avg 0.802 0.798 0.798 218
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.838 0.632 0.720 106
1 0.717 0.884 0.792 112
accuracy 0.761 218
macro avg 0.777 0.758 0.756 218
weighted avg 0.776 0.761 0.757 218
# Spilt data
train_convos, test_convos = train_test_split(list(wiki_corpus_binary.iter_utterances())
, test_size=0.1, shuffle=False)
# Iterate over utterances using their corresponding id's and mark them as train and test
for utt in train_convos:
utt.meta['train_test_type'] = 'train'
for utt in test_convos:
utt.meta['train_test_type'] = 'test'
# Fit the model with training set labels only. Using a lambda function filter such training samples
tf_idf_vect_clf.fit(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
#Using a lambda function predict the outcome variable for only test set
tf_idf_vect_clf.transform(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')
# Print classification report
print("*********************** Classification Report (Standard Logisitic Regression)*************************")
print(tf_idf_vect_clf.classification_report(wiki_corpus_binary,
selector=lambda utt: utt.meta['train_test_type'] == 'test'))
*********************** Classification Report (Standard Logisitic Regression)*************************
precision recall f1-score support
False 0.84 0.79 0.82 106
True 0.81 0.86 0.83 112
accuracy 0.83 218
macro avg 0.83 0.82 0.83 218
weighted avg 0.83 0.83 0.83 218
tf_idf_vect_clf_mb.fit(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
tf_idf_vect_clf_mb.transform(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')
print("*********************** Classification Report (Naive Bayes)*************************")
print(tf_idf_vect_clf_mb.classification_report(wiki_corpus_binary,
selector=lambda utt: utt.meta['train_test_type'] == 'test'))
*********************** Classification Report (Naive Bayes)*************************
precision recall f1-score support
False 0.81 0.59 0.68 106
True 0.69 0.87 0.77 112
accuracy 0.73 218
macro avg 0.75 0.73 0.73 218
weighted avg 0.75 0.73 0.73 218
tf_idf_vect_clf_dt.fit(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
tf_idf_vect_clf_dt.transform(wiki_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')
print(tf_idf_vect_clf_dt.classification_report(wiki_corpus_binary,
selector=lambda utt: utt.meta['train_test_type'] == 'test'))
precision recall f1-score support
False 0.69 0.73 0.71 106
True 0.73 0.69 0.71 112
accuracy 0.71 218
macro avg 0.71 0.71 0.71 218
weighted avg 0.71 0.71 0.71 218
tf_idf_vect_clf.summarize(wiki_corpus_binary).head(5)
| prediction | pred_score | |
|---|---|---|
| id | ||
| 620707 | True | 1.0 |
| 62454 | True | 1.0 |
| 153143 | True | 1.0 |
| 201933 | True | 1.0 |
| 486441 | True | 1.0 |
tf_idf_vect_clf_mb.summarize(wiki_corpus_binary).head(5)
| prediction | pred_score | |
|---|---|---|
| id | ||
| 620707 | True | 1.0 |
| 62454 | True | 1.0 |
| 153143 | True | 1.0 |
| 201933 | True | 1.0 |
| 486441 | True | 1.0 |
tf_idf_vect_clf_dt.summarize(wiki_corpus_binary).head(10)
| prediction | pred_score | |
|---|---|---|
| id | ||
| 620707 | True | 1.0 |
| 62454 | True | 1.0 |
| 153143 | True | 1.0 |
| 201933 | True | 1.0 |
| 486441 | True | 1.0 |
| 628941 | True | 1.0 |
| 203996 | True | 1.0 |
| 556495 | True | 1.0 |
| 600849 | True | 1.0 |
| 439715 | True | 1.0 |
To print run TF-IDF on Stack Exchange run the following function. This function uses inbuilt ConvoKit Vector Classifier which takes vector as input and by default runs Standard Logistic Regression Classifier which can changed to run different classifiers by altering the parameters. However the function uses extremely slow . If cross Validation scores are not required simply use the next function instead i.e. stackExchangeTFIDF(). All the metrics are the same irrespective of the method used
def stackTFIDFVectoriConvo():
from convokit import TextParser, PolitenessStrategies
from convokit.expected_context_framework import ColNormedTfidfTransformer
# Call both Text Parser and Politeness Strategies Tranformer
parser = TextParser(verbosity = 3000)
ps = PolitenessStrategies()
#Load corpus and filter out utterances with class label of 0
_corpus = Corpus(download("stack-exchange-politeness-corpus"))
_corpus_binary = Corpus(utterances = [utt for utt in _corpus.iter_utterances() if utt.meta['Binary']!= 0 ])
# tranform the corpus using the transformers
_corpus_binary = parser.transform(_corpus_binary)
_corpus_binary = ps.transform(_corpus_binary, markers = True)
df_binary = _corpus_binary.get_utterances_dataframe()
display(df_binary.head())
# Use tf-idf transformer to convert utterance text to numeric data using term frequency and inverse frequency
# in a document
td_idf = ColNormedTfidfTransformer(input_field = 'text', output_field='col_normed_tfidf', model=None )
td_idf.fit_transform(_corpus_binary)
# Extract the matrix produced by tf-idf tranformer
td_idf_vector = _corpus_binary.get_vector_matrix('col_normed_tfidf').to_dataframe()
display(td_idf_vector.head())
model_names = ["Logisitc Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
"MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"]
# Use ML/DL classifiers
lr_clf = LogisticRegression()
mb_clf = MultinomialNB()
svm_clf_rbf = SVC(kernel = 'rbf', probability= True)
svm_clf_poly = SVC(kernel = 'poly',probability= True )
knn_clf = KNeighborsClassifier(n_neighbors = 3, weights='uniform',
metric='minkowski')
dtree_clf = DecisionTreeClassifier(criterion ="entropy")
mlp_clf_relu = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,
activation='relu', random_state = 21)
mlp_clf_tanh = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,
activation='tanh', random_state = 22)
# precp_clf = Perceptron()
clf_models = [lr_clf, mb_clf , svm_clf_rbf, svm_clf_poly, knn_clf, dtree_clf, mlp_clf_relu, mlp_clf_tanh]
# Call the vector classifier of ConvoKit frame work
tf_idf_vect_clf_std = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf',
labeller=lambda utt: utt.meta['Binary'] == 1)
mytf_idf_lst= []
# Call Vector Classifier for all the models. Using a loop instead, create model object and store it in a list
for models in clf_models:
tf_idf_vect_clf = VectorClassifier(obj_type="utterance", vector_name='col_normed_tfidf', clf = models,
labeller=lambda utt: utt.meta['Binary'] == 1)
mytf_idf_lst.append(tf_idf_vect_clf)
score_list = list()
score_std = tf_idf_vect_clf_std.evaluate_with_cv(_corpus_binary)
# Calculate CV scores for all of the models
for i in mytf_idf_lst:
score_ = np.mean(i.evaluate_with_cv(_corpus_binary))
score_list.append(score_)
score_list.insert(0,np.mean(score_std))
frame = pd.DataFrame({ 'Cross Validation Mean score ': score_list}, index = model_names)
display(frame)
# As done earlier split the data into training and testing test using utterance ids
train_convos, test_convos = train_test_split(list(_corpus_binary.iter_utterances())
, test_size=0.1, shuffle=False)
for utt in train_convos:
utt.meta['train_test_type'] = 'train'
for utt in test_convos:
utt.meta['train_test_type'] = 'test'
tf_idf_vect_clf_std.fit(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
tf_idf_vect_clf_std.transform(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')
print("Classification Report for Standard Logistic Regression ")
print(tf_idf_vect_clf_std.classification_report(_corpus_binary,
selector=lambda utt: utt.meta['train_test_type'] == 'test'))
# Print classification report for all the models generated and stored in a list
for i , model in enumerate(mytf_idf_lst):
model.fit(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'train')
model.transform(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test')
name = model_names[i]
print(model)
print(f"*******************Classification Report for {name}*********************************")
print(model.classification_report(_corpus_binary, selector=lambda utt: utt.meta['train_test_type'] == 'test'))
stackTFIDFVectoriConvo()
Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus 3000/3302 utterances processed 3302/3302 utterances processed
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 4 | NOT_RECORDED | Is `A` a global variable? What is x? | user | None | 4 | 0.508284 | 1 | {'A3OW54MEVDKXJL': 17, 'A2RDZ580VXUO1X': 18, '... | [{'rt': 0, 'toks': [{'tok': 'is', 'tag': 'VBZ'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 5 | NOT_RECORDED | This is a very confusing question! How are yo... | user | None | 5 | -0.393623 | -1 | {'A2WKPCZU4U110T': 16, 'A1BS64O3JY0YJ4': 14, '... | [{'rt': 1, 'toks': [{'tok': 'this', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 6 | NOT_RECORDED | Why not using `isnan()` from math.h? Any speci... | user | None | 6 | -0.689701 | -1 | {'AL97SCCNKZILP': 7, 'A3E157ZN8XPUKJ': 20, 'A2... | [{'rt': 2, 'toks': [{'tok': 'why', 'tag': 'WRB... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 9 | NOT_RECORDED | Does your project involve some graphical user ... | user | None | 9 | 0.519398 | 1 | {'A2UFD1I8ZO1V4G': 17, 'A3MMLCBV2W3BP9': 13, '... | [{'rt': 3, 'toks': [{'tok': 'does', 'tag': 'VB... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 11 | NOT_RECORDED | Usually compilers should generate a good code ... | user | None | 11 | 0.631237 | 1 | {'A2TMSM19YCEXLE': 20, 'A28TXBSZPWMEU9': 15, '... | [{'rt': 3, 'toks': [{'tok': 'usually', 'tag': ... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| ! | !! | != | " | "" | ""this | "$thing->edit | "(pseudo) | "*i | "+1" | ... | μf, | — | “blog”? | “good” | “ie6 | “k-cnf” | “section” | “synchronize | ℽℼℾℿ | か | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 11 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 12405 columns
Initialized default classification model (standard scaled logistic regression). Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done. Running a cross-validated evaluation...Done.
| Cross Validation Mean score | |
|---|---|
| Logisitc Regression | 0.594489 |
| Naive Bayes | 0.606306 |
| Support Vector Machine (Kernel= (RBF)) | 0.570564 |
| Support Vector Machine (Kernel = Polynomial) | 0.553598 |
| K-Nearest Neighbour | 0.502103 |
| Decision Tree | 0.503640 |
| MLP(Multi-Layer Preceptron(Relu)) | 0.558448 |
| MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) | 0.588748 |
| Perceptron | 0.586320 |
Classification Report for Standard Logistic Regression
precision recall f1-score support
False 0.56 0.55 0.56 159
True 0.59 0.61 0.60 172
accuracy 0.58 331
macro avg 0.58 0.58 0.58 331
weighted avg 0.58 0.58 0.58 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B46E3A48>
*******************Classification Report for Logisitc Regression *********************************
precision recall f1-score support
False 0.53 0.65 0.59 159
True 0.59 0.47 0.52 172
accuracy 0.56 331
macro avg 0.56 0.56 0.55 331
weighted avg 0.56 0.56 0.55 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185DD0F0D88>
*******************Classification Report for Naive Bayes*********************************
precision recall f1-score support
False 0.50 0.57 0.53 159
True 0.55 0.48 0.51 172
accuracy 0.52 331
macro avg 0.52 0.52 0.52 331
weighted avg 0.53 0.52 0.52 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861A8C8>
*******************Classification Report for Support Vector Machine (Kernel= (RBF))*********************************
precision recall f1-score support
False 0.50 0.58 0.54 159
True 0.54 0.45 0.49 172
accuracy 0.52 331
macro avg 0.52 0.52 0.52 331
weighted avg 0.52 0.52 0.51 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861AAC8>
*******************Classification Report for Support Vector Machine (Kernel = Polynomial)*********************************
precision recall f1-score support
False 0.48 1.00 0.65 159
True 0.00 0.00 0.00 172
accuracy 0.48 331
macro avg 0.24 0.50 0.32 331
weighted avg 0.23 0.48 0.31 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861AA48>
*******************Classification Report for K-Nearest Neighbour*********************************
precision recall f1-score support
False 0.48 1.00 0.65 159
True 0.00 0.00 0.00 172
accuracy 0.48 331
macro avg 0.24 0.50 0.32 331
weighted avg 0.23 0.48 0.31 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185B861AA88>
*******************Classification Report for Decision Tree*********************************
precision recall f1-score support
False 0.53 0.60 0.56 159
True 0.58 0.50 0.54 172
accuracy 0.55 331
macro avg 0.55 0.55 0.55 331
weighted avg 0.55 0.55 0.55 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185DD0F3EC8>
*******************Classification Report for MLP(Multi-Layer Preceptron(Relu))*********************************
precision recall f1-score support
False 0.51 0.57 0.54 159
True 0.55 0.48 0.51 172
accuracy 0.53 331
macro avg 0.53 0.53 0.53 331
weighted avg 0.53 0.53 0.52 331
<convokit.classifier.vectorClassifier.VectorClassifier object at 0x00000185DD0F3F08>
*******************Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent))*********************************
precision recall f1-score support
False 0.51 0.61 0.56 159
True 0.56 0.47 0.51 172
accuracy 0.53 331
macro avg 0.54 0.54 0.53 331
weighted avg 0.54 0.53 0.53 331
def stackExchangeTFIDF():
from convokit import TextParser, PolitenessStrategies
from convokit.expected_context_framework import ColNormedTfidfTransformer
# Call both Text Parser and Politeness Strategies Tranformer
parser = TextParser(verbosity = 3000)
ps = PolitenessStrategies()
#Load corpus and filter out utterances with class label of 0
_corpus = Corpus(download("stack-exchange-politeness-corpus"))
_corpus_binary = Corpus(utterances = [utt for utt in _corpus.iter_utterances() if utt.meta['Binary']!= 0 ])
# tranform the corpus using the transformers
_corpus_binary = parser.transform(_corpus_binary)
_corpus_binary = ps.transform(_corpus_binary, markers = True)
df_binary = _corpus_binary.get_utterances_dataframe()
display(df_binary.head())
# Use tf-idf transformer to convert utterance text to numeric data using term frequency and inverse frequency
# in a document
td_idf = ColNormedTfidfTransformer(input_field = 'text', output_field='col_normed_tfidf', model=None )
td_idf.fit_transform(_corpus_binary)
# Extract the matrix produced by tf-idf tranformer. Remap 0:ImPolite 1:Polite instead of -1 and 1 respectively
td_idf_vector = _corpus_binary.get_vector_matrix('col_normed_tfidf').to_dataframe()
binary_ = df_binary['meta.Binary'].astype("category")
changedicti_ = {-1 : 0 , 1 : 1 }
binary_.replace(changedicti_ , inplace = True)
display(td_idf_vector.head())
model_names = ["Logisitc Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour","Decision Tree",
"MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))","Perceptron"]
lr_clf = LogisticRegression()
mb_clf = MultinomialNB()
svm_clf_rbf = SVC(kernel = 'rbf', probability= True)
svm_clf_poly = SVC(kernel = 'poly', probability= True )
knn_clf = KNeighborsClassifier(n_neighbors = 3, weights='uniform',
metric='minkowski')
dtree_clf = DecisionTreeClassifier(criterion ="entropy")
mlp_clf_relu = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,
activation='relu', random_state = 21)
mlp_clf_tanh = MLPClassifier(hidden_layer_sizes=(30,30), max_iter = 1000,
activation='tanh', random_state = 22)
precp_clf = Perceptron()
clf_models = [mb_clf , svm_clf_rbf, svm_clf_poly, knn_clf, dtree_clf, mlp_clf_relu, mlp_clf_tanh, precp_clf]
# Split data using spilt_data() defined above
X_train_tf, X_test_tf, y_train_tf, y_test_tf = split_data(pd.concat([td_idf_vector, binary_],axis=1),
0.90, pd.concat([td_idf_vector,binary_],axis=1).shape)
#Fit models
tf_idf_models = generateModels(X_train_tf,y_train_tf)
count = 0
for model in tf_idf_models:
performPrediction(model, X_test_tf, y_test_tf, "Paired", model_names[count])
count += 1
stackExchangeTFIDF()
Dataset already exists at C:\Users\pulki\.convokit\downloads\stack-exchange-politeness-corpus 3000/3302 utterances processed 3302/3302 utterances processed
| timestamp | text | speaker | reply_to | conversation_id | meta.Normalized Score | meta.Binary | meta.Annotations | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 4 | NOT_RECORDED | Is `A` a global variable? What is x? | user | None | 4 | 0.508284 | 1 | {'A3OW54MEVDKXJL': 17, 'A2RDZ580VXUO1X': 18, '... | [{'rt': 0, 'toks': [{'tok': 'is', 'tag': 'VBZ'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 5 | NOT_RECORDED | This is a very confusing question! How are yo... | user | None | 5 | -0.393623 | -1 | {'A2WKPCZU4U110T': 16, 'A1BS64O3JY0YJ4': 14, '... | [{'rt': 1, 'toks': [{'tok': 'this', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 6 | NOT_RECORDED | Why not using `isnan()` from math.h? Any speci... | user | None | 6 | -0.689701 | -1 | {'AL97SCCNKZILP': 7, 'A3E157ZN8XPUKJ': 20, 'A2... | [{'rt': 2, 'toks': [{'tok': 'why', 'tag': 'WRB... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 9 | NOT_RECORDED | Does your project involve some graphical user ... | user | None | 9 | 0.519398 | 1 | {'A2UFD1I8ZO1V4G': 17, 'A3MMLCBV2W3BP9': 13, '... | [{'rt': 3, 'toks': [{'tok': 'does', 'tag': 'VB... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 11 | NOT_RECORDED | Usually compilers should generate a good code ... | user | None | 11 | 0.631237 | 1 | {'A2TMSM19YCEXLE': 20, 'A28TXBSZPWMEU9': 15, '... | [{'rt': 3, 'toks': [{'tok': 'usually', 'tag': ... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| ! | !! | != | " | "" | ""this | "$thing->edit | "(pseudo) | "*i | "+1" | ... | μf, | — | “blog”? | “good” | “ie6 | “k-cnf” | “section” | “synchronize | ℽℼℾℿ | か | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 11 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 12405 columns
----------------Confusion Matrix for Logisitc Regression Classifier--------------------
Classification Report for Logisitc Regression Classifier
precision recall f1-score support
0 0.531 0.654 0.586 159
1 0.593 0.465 0.521 172
accuracy 0.556 331
macro avg 0.562 0.560 0.554 331
weighted avg 0.563 0.556 0.552 331
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.503 0.566 0.533 159
1 0.546 0.483 0.512 172
accuracy 0.523 331
macro avg 0.524 0.524 0.522 331
weighted avg 0.525 0.523 0.522 331
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.497 0.585 0.538 159
1 0.542 0.453 0.494 172
accuracy 0.517 331
macro avg 0.519 0.519 0.516 331
weighted avg 0.520 0.517 0.515 331
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.480 1.000 0.649 159
1 0.000 0.000 0.000 172
accuracy 0.480 331
macro avg 0.240 0.500 0.324 331
weighted avg 0.231 0.480 0.312 331
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.480 1.000 0.649 159
1 0.000 0.000 0.000 172
accuracy 0.480 331
macro avg 0.240 0.500 0.324 331
weighted avg 0.231 0.480 0.312 331
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.522 0.585 0.552 159
1 0.569 0.506 0.535 172
accuracy 0.544 331
macro avg 0.546 0.545 0.544 331
weighted avg 0.546 0.544 0.543 331
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.506 0.572 0.537 159
1 0.550 0.483 0.514 172
accuracy 0.526 331
macro avg 0.528 0.527 0.525 331
weighted avg 0.528 0.526 0.525 331
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.513 0.610 0.557 159
1 0.563 0.465 0.510 172
accuracy 0.535 331
macro avg 0.538 0.538 0.534 331
weighted avg 0.539 0.535 0.533 331
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.560 0.409 0.473 159
1 0.563 0.703 0.625 172
accuracy 0.562 331
macro avg 0.562 0.556 0.549 331
weighted avg 0.562 0.562 0.552 331
Using the above Linguistic Marker models for Wikipedia we can annotate our Wiki Talk Corpus for being polite or not. As Wiki Talk Corpus contains 400,0000 utterances annotating each and every utterance is not possible .As mentioned in ('research paper') we can use Linguistic marker model built on 2718 utterances to annotate the data. We can then combine 2718 utterances obtained from wiki with 3002 utterances and build a better model. However for this experiment Wiki Politeness Linguistic Marker Model will be used
As we dont have gold standard labels available for our dataset Techniques such semi supervised learning in such scenrios. First predict the labels using Wikipedia Politeness Corpus. The use those labels as gold standard labels to built models on Wiki-Talk Corpus and evaluate performance. For this short demo only Naive Bayes classifier will be used to predict labels for utterance level information in Wiki - Talk Corpus
def load_wiki_corpus_full():
from convokit import TextParser, PolitenessStrategies
parser = TextParser(verbosity = 10000)
ps = PolitenessStrategies()
# As Corpus is huge use only first 50,000 utterances
wiki_corpus = Corpus(download('wiki-corpus'), utterance_end_index = 50000)
wiki_corpus= parser.transform(wiki_corpus)
wiki_corpus = ps.transform(wiki_corpus, markers = True)
df_wiki_ = wiki_corpus.get_utterances_dataframe()
return wiki_corpus, df_wiki_
wiki_full , df_wiki_utt = load_wiki_corpus_full()
Downloading wiki-corpus to C:\Users\pulki\capstone project\march 11th 2021 politeness\wiki-corpus Downloading wiki-corpus from http://zissou.infosci.cornell.edu/convokit/datasets/wiki-corpus/wiki-corpus.zip (238.4MB)... Done 10000/50001 utterances processed 20000/50001 utterances processed 30000/50001 utterances processed 40000/50001 utterances processed 50000/50001 utterances processed 50001/50001 utterances processed
df_wiki_utt[0:3]
| timestamp | text | speaker | reply_to | conversation_id | meta.is-admin | meta.parsed | meta.politeness_strategies | meta.politeness_markers | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 524288 | 1.189190940E09 | You should look at all of the point on the tem... | Frightner | None | 524288 | False | [{'rt': 20, 'toks': [{'tok': 'you', 'tag': 'PR... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 524289 | 1.189204860E09 | Yes I agree. The law permits usage of document... | Revizionist | None | 524288 | False | [{'rt': 2, 'toks': [{'tok': 'yes', 'tag': 'UH'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
| 1 | 1.310744280E09 | Yes, that's good. Revathy's page looked very r... | Johannes003 | None | 1 | False | [{'rt': 3, 'toks': [{'tok': 'yes', 'tag': 'UH'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [] |
wiki_full.get_speakers_dataframe()
| vectors | meta.is-admin | meta.edit-count | meta.coord | |
|---|---|---|---|---|
| id | ||||
| Frightner | [] | False | 1322 | NaN |
| Revizionist | [] | False | 1716 | NaN |
| Johannes003 | [] | False | 8283 | NaN |
| Michael-Billa | [] | False | 396 | NaN |
| AnonEMouse | [] | True | 13150 | {'Kwork': {'article': 0.0, 'auxverb': 0.0, 'co... |
| ... | ... | ... | ... | ... |
| {unknown-1133} | [] | False | unknown | NaN |
| Kelly Martin | [] | True | 17726 | NaN |
| Cretanpride | [] | False | 192 | NaN |
| CaveatLector | [] | False | 2196 | NaN |
| Huntster | [] | True | 27608 | NaN |
9331 rows × 4 columns
utterance_ids = wiki_full.get_utterance_ids()
rows = list()
for ids in utterance_ids:
rows.append(wiki_full.get_utterance(ids).meta['politeness_strategies'])
_wiki_features = pd.DataFrame(rows, index = utterance_ids)
_wiki_features
| feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==HASHEDGE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Hedges== | feature_politeness_==Factuality== | feature_politeness_==Deference== | feature_politeness_==Gratitude== | feature_politeness_==Apologizing== | feature_politeness_==1st_person_pl.== | ... | feature_politeness_==1st_person_start== | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==HASPOSITIVE== | feature_politeness_==HASNEGATIVE== | feature_politeness_==SUBJUNCTIVE== | feature_politeness_==INDICATIVE== | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 524288 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 524289 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | ... | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 266783 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| 552089 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 552090 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 289946 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 289947 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
50001 rows × 21 columns
Predict the gold standard labels
gold_labels = mb_clf.predict(_wiki_features)
gold_labl_series = pd.Series(gold_labels , index = utterance_ids, name = 'meta.Binary')
# get value counts for each class labels
gold_labl_series.value_counts()
1 40137 0 9864 Name: meta.Binary, dtype: int64
df_ = pd.concat([_wiki_features, gold_labl_series] , axis = 1)
df_
| feature_politeness_==Please== | feature_politeness_==Please_start== | feature_politeness_==HASHEDGE== | feature_politeness_==Indirect_(btw)== | feature_politeness_==Hedges== | feature_politeness_==Factuality== | feature_politeness_==Deference== | feature_politeness_==Gratitude== | feature_politeness_==Apologizing== | feature_politeness_==1st_person_pl.== | ... | feature_politeness_==2nd_person== | feature_politeness_==2nd_person_start== | feature_politeness_==Indirect_(greeting)== | feature_politeness_==Direct_question== | feature_politeness_==Direct_start== | feature_politeness_==HASPOSITIVE== | feature_politeness_==HASNEGATIVE== | feature_politeness_==SUBJUNCTIVE== | feature_politeness_==INDICATIVE== | meta.Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 524288 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 524289 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 3 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 266783 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 |
| 552089 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 552090 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 289946 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 289947 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
50001 rows × 22 columns
The information above shows that the for Polite class label we have 40137 and for Impolite class label we have 9864 observations. As the classed are not balanced with observations associated to Impolite Class account only for 20% the model will be a weak classifier. We will try to address this issue using SMOTE analysis . Refer [3]
(9864/(40137+9864))*100
19.72760544789104
(40137/(40137+9864))*100
80.27239455210896
9864/40137
0.24575827789819868
Now use these to build a model and train various classifiers on it
X_train_w, X_test_w, y_train_w, y_test_w = split_data(df_, 0.80, df_.shape)
models_ = generateModels(X_train_w, y_train_w)
def getClassifiStats(models , X_test, y_test):
model_names = ["Logistic Regression ", "Naive Bayes", "Support Vector Machine (Kernel= (RBF))",
"Support Vector Machine (Kernel = Polynomial)","K-Nearest Neighbour",
"Decision Tree", "MLP(Multi-Layer Preceptron(Relu))", "MLP (Multi-Layer Preceptron(Hyberbolic Tangent))",
"Perceptron"
]
count = 0
for i in models:
performPrediction(i , X_test, y_test, "Paired", model_names[count])
count += 1
getClassifiStats(models_ , X_test_w, y_test_w)
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.983 0.983 0.983 1923
1 0.996 0.996 0.996 8078
accuracy 0.994 10001
macro avg 0.990 0.990 0.990 10001
weighted avg 0.994 0.994 0.994 10001
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.884 0.261 0.403 1923
1 0.849 0.992 0.915 8078
accuracy 0.851 10001
macro avg 0.867 0.626 0.659 10001
weighted avg 0.856 0.851 0.817 10001
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.997 0.995 0.996 1923
1 0.999 0.999 0.999 8078
accuracy 0.999 10001
macro avg 0.998 0.997 0.998 10001
weighted avg 0.998 0.999 0.998 10001
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.986 0.996 0.991 1923
1 0.999 0.997 0.998 8078
accuracy 0.997 10001
macro avg 0.993 0.996 0.994 10001
weighted avg 0.997 0.997 0.997 10001
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.990 0.981 0.985 1923
1 0.995 0.998 0.996 8078
accuracy 0.994 10001
macro avg 0.992 0.989 0.991 10001
weighted avg 0.994 0.994 0.994 10001
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.996 0.994 0.995 1923
1 0.999 0.999 0.999 8078
accuracy 0.998 10001
macro avg 0.997 0.996 0.997 10001
weighted avg 0.998 0.998 0.998 10001
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.999 0.999 0.999 1923
1 1.000 1.000 1.000 8078
accuracy 1.000 10001
macro avg 0.999 0.999 0.999 10001
weighted avg 1.000 1.000 1.000 10001
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.999 0.998 0.999 1923
1 1.000 1.000 1.000 8078
accuracy 1.000 10001
macro avg 0.999 0.999 0.999 10001
weighted avg 0.999 1.000 1.000 10001
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.998 0.982 0.990 1923
1 0.996 1.000 0.998 8078
accuracy 0.996 10001
macro avg 0.997 0.991 0.994 10001
weighted avg 0.996 0.996 0.996 10001
SMOTE is an oversampling technique where the synthetic samples are generated for the minority class. There are various variations of this technique. We however used the most basic version of it . For More information refer([3]) and https://www.analyticsvidhya.com/blog/2020/10/overcoming-class-imbalance-using-smote-techniques/
pip install imblearn
Requirement already satisfied: imblearn in c:\users\pulki\anaconda3\lib\site-packages (0.0) Requirement already satisfied: imbalanced-learn in c:\users\pulki\anaconda3\lib\site-packages (from imblearn) (0.9.0) Requirement already satisfied: scikit-learn>=1.0.1 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (1.0.2) Requirement already satisfied: numpy>=1.14.6 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (1.21.6) Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (3.1.0) Requirement already satisfied: joblib>=0.11 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (0.14.1) Requirement already satisfied: scipy>=1.1.0 in c:\users\pulki\anaconda3\lib\site-packages (from imbalanced-learn->imblearn) (1.4.1) Note: you may need to restart the kernel to use updated packages.
print("Before OverSampling, counts of labels : {}".format(y_train_w.value_counts()))
Before OverSampling, counts of labels : meta.Binary 1 32059 0 7941 dtype: int64
from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state = 2)
X_train_res, y_train_res = sm.fit_resample(X_train_w, y_train_w)
print('Before OverSampling, the shape of train_X: {}'.format(X_train_w.shape))
print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))
print('After OverSampling, the shape of train_y: {} \n'.format(y_train_res.shape))
Before OverSampling, the shape of train_X: (40000, 21) After OverSampling, the shape of train_X: (64118, 21) After OverSampling, the shape of train_y: (64118, 1)
print("After OverSampling, counts of labels : {}".format(y_train_res.value_counts()))
After OverSampling, counts of labels : meta.Binary 0 32059 1 32059 dtype: int64
models_SMOTE = generateModels(X_train_res , y_train_res)
getClassifiStats(models_SMOTE , X_test_w, y_test_w)
----------------Confusion Matrix for Logistic Regression Classifier--------------------
Classification Report for Logistic Regression Classifier
precision recall f1-score support
0 0.927 0.992 0.958 1923
1 0.998 0.981 0.990 8078
accuracy 0.983 10001
macro avg 0.962 0.986 0.974 10001
weighted avg 0.984 0.983 0.984 10001
----------------Confusion Matrix for Naive Bayes Classifier--------------------
Classification Report for Naive Bayes Classifier
precision recall f1-score support
0 0.753 0.966 0.846 1923
1 0.991 0.924 0.957 8078
accuracy 0.932 10001
macro avg 0.872 0.945 0.901 10001
weighted avg 0.945 0.932 0.935 10001
----------------Confusion Matrix for Support Vector Machine (Kernel= (RBF)) Classifier--------------------
Classification Report for Support Vector Machine (Kernel= (RBF)) Classifier
precision recall f1-score support
0 0.997 0.995 0.996 1923
1 0.999 0.999 0.999 8078
accuracy 0.998 10001
macro avg 0.998 0.997 0.997 10001
weighted avg 0.998 0.998 0.998 10001
----------------Confusion Matrix for Support Vector Machine (Kernel = Polynomial) Classifier--------------------
Classification Report for Support Vector Machine (Kernel = Polynomial) Classifier
precision recall f1-score support
0 0.996 0.998 0.997 1923
1 1.000 0.999 0.999 8078
accuracy 0.999 10001
macro avg 0.998 0.998 0.998 10001
weighted avg 0.999 0.999 0.999 10001
----------------Confusion Matrix for K-Nearest Neighbour Classifier--------------------
Classification Report for K-Nearest Neighbour Classifier
precision recall f1-score support
0 0.988 0.981 0.985 1923
1 0.996 0.997 0.996 8078
accuracy 0.994 10001
macro avg 0.992 0.989 0.991 10001
weighted avg 0.994 0.994 0.994 10001
----------------Confusion Matrix for Decision Tree Classifier--------------------
Classification Report for Decision Tree Classifier
precision recall f1-score support
0 0.996 0.994 0.995 1923
1 0.999 0.999 0.999 8078
accuracy 0.998 10001
macro avg 0.997 0.997 0.997 10001
weighted avg 0.998 0.998 0.998 10001
----------------Confusion Matrix for MLP(Multi-Layer Preceptron(Relu)) Classifier--------------------
Classification Report for MLP(Multi-Layer Preceptron(Relu)) Classifier
precision recall f1-score support
0 0.999 0.997 0.998 1923
1 0.999 1.000 1.000 8078
accuracy 0.999 10001
macro avg 0.999 0.999 0.999 10001
weighted avg 0.999 0.999 0.999 10001
----------------Confusion Matrix for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier--------------------
Classification Report for MLP (Multi-Layer Preceptron(Hyberbolic Tangent)) Classifier
precision recall f1-score support
0 0.997 1.000 0.998 1923
1 1.000 0.999 1.000 8078
accuracy 0.999 10001
macro avg 0.998 1.000 0.999 10001
weighted avg 0.999 0.999 0.999 10001
----------------Confusion Matrix for Perceptron Classifier--------------------
Classification Report for Perceptron Classifier
precision recall f1-score support
0 0.993 0.995 0.994 1923
1 0.999 0.998 0.999 8078
accuracy 0.998 10001
macro avg 0.996 0.997 0.996 10001
weighted avg 0.998 0.998 0.998 10001
The Conversational Data Collected cannot only be used to predict politeness outcome. It can be used to answer question such as "DO admins communicate with users more than non admins ?" or DO users communicate more to non admin or admin. This can be answered using Coordination scores provided by Coordination Transformer, a Transformer provided by ConvoKit.
# Load arc dependencies as it will include information such as dependency structure
wiki_full.load_info('utterance',['arcs_censored'])
# change arcs to list of arc_censored as the Coordination doesnt accept list as input. Input has to be string
from convokit.text_processing import TextProcessor
join_arcs = TextProcessor(input_field='arcs_censored', output_field='arcs',
proc_fn=lambda sents: '\n'.join(sents))
wiki_full = join_arcs.transform(wiki_full)
coord = convokit.Coordination()
coord.fit(wiki_full)
Coordination scores get calculated using liwc-categories. For instance an utterance has auxverb, conjuction, preposition etc which is associated to a particular speaker. Suppose this speaker uses a lot of these morphological features. These features can be characaterstic of a speaker. These morphological features aggregated and key characteristic features specific to particular users can be identified. For example, speaker 'A' ,uses of auxverb , pronouns or other linguistic markers, which happens to be an admin. Speaker b,c and d also have this in common. By using aggregated scores we can conclude that admins use a lot of auxverb, pronouns etc in there replies or text
wiki_full.get_utterances_dataframe()
| timestamp | text | speaker | reply_to | conversation_id | meta.is-admin | meta.parsed | meta.politeness_strategies | meta.politeness_markers | meta.arcs_censored | meta.arcs | meta.liwc-categories | vectors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 524288 | 1.189190940E09 | You should look at all of the point on the tem... | Frightner | None | 524288 | False | [{'rt': 20, 'toks': [{'tok': 'you', 'tag': 'PR... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [says_* says_also says_is says_look] | says_* says_also says_is says_look | {auxverb, ipron, conj, preps, quant, ppron, ar... | [] |
| 524289 | 1.189204860E09 | Yes I agree. The law permits usage of document... | Revizionist | None | 524288 | False | [{'rt': 2, 'toks': [{'tok': 'yes', 'tag': 'UH'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [agree_* agree_yes yes>*, permits_*, there>* w... | agree_* agree_yes yes>*\npermits_*\nthere>* wa... | {article, adverb, auxverb, ipron, conj, preps,... | [] |
| 1 | 1.310744280E09 | Yes, that's good. Revathy's page looked very r... | Johannes003 | None | 1 | False | [{'rt': 3, 'toks': [{'tok': 'yes', 'tag': 'UH'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | ['s_* 's_good 's_yes yes>*, 's_* 's_looked 's_... | 's_* 's_good 's_yes yes>*\n's_* 's_looked 's_used | {article, adverb, auxverb, preps, conj, ipron,... | [] |
| 2 | 1.310746500E09 | Nagma'a site, at least that filmography page, ... | Johannes003 | 1 | 1 | False | [{'rt': 9, 'toks': [{'tok': "nagma'a", 'tag': ... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [looks_* looks_taken, remove_* remove_did remo... | looks_* looks_taken\nremove_* remove_did remov... | {auxverb, ipron, conj, preps, quant, ppron, ar... | [] |
| 3 | 1.310751180E09 | I don't think there are many such official rel... | Johannes003 | 2 | 1 | False | [{'rt': 3, 'toks': [{'tok': 'i', 'tag': 'PRP',... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [think_* think_are think_do, are_* are_not are... | think_* think_are think_do\nare_* are_not are_... | {auxverb, ipron, conj, preps, quant, ppron, ar... | [] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 266783 | 1.176454500E09 | I felt one line on language of administration\... | Dineshkannambadi | None | 266777 | False | [{'rt': 1, 'toks': [{'tok': 'i', 'tag': 'PRP',... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [felt_* felt_give, think_* think_do think_what... | felt_* felt_give\nthink_* think_do think_what ... | {article, auxverb, conj, ipron, preps, ppron} | [] |
| 552089 | 1.143592380E09 | It looks like the [[William Shakespeare]] expe... | Rklawton | None | 552089 | False | [{'rt': 1, 'toks': [{'tok': 'it', 'tag': 'PRP'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [looks_* looks_like, turning_* turning_is turn... | looks_* looks_like\nturning_* turning_is turni... | {auxverb, ipron, conj, preps, quant, ppron, ar... | [] |
| 552090 | 1.143603840E09 | It's not an experiment. This is a wiki. People... | Splash | 552089 | 552089 | True | [{'rt': 1, 'toks': [{'tok': 'it', 'tag': 'PRP'... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | ['s_* 's_not, is_*, edit_* edit_can] | 's_* 's_not\nis_*\nedit_* edit_can | {article, auxverb, ipron} | [] |
| 289946 | 1.156854120E09 | That's odd. Somehow, I came across one of tha... | Mike 7 | None | 289946 | False | [{'rt': 1, 'toks': [{'tok': 'that', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | ['s_* 's_odd, came_* came_across came_believe,... | 's_* 's_odd\ncame_* came_across came_believe\n... | {auxverb, ipron, conj, preps, quant, ppron, ar... | [] |
| 289947 | 1.156854600E09 | That could be the case. I've seen a few of th... | Mike 7 | 289946 | 289946 | False | [{'rt': 2, 'toks': [{'tok': 'that', 'tag': 'DT... | {'feature_politeness_==Please==': 0, 'feature_... | {'politeness_markers_==Please==': [], 'politen... | [be_* be_could, seen_'ve seen_*] | be_* be_could\nseen_'ve seen_* | {article, auxverb, preps, ipron, quant, ppron} | [] |
50001 rows × 13 columns
c = coord.summarize(wiki_full)
c
{Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '7098'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'CycloneGU'}): {'article': 0.0,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '15258'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'SynergyStar'}): {'article': -0.020000000000000018,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '1'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Gerardw'}): {'article': 0.0,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '126074'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Plastikspork'}): {'article': -0.09999999999999998,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '29884'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Aleksandr Grigoryev'}): {'article': -0.08333333333333337,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '2756'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Mr. Neutron'}): {'article': 0.0,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '26998'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Cunard'}): {'article': 0.0,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '23015'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Literaturegeek'}): {'article': -0.08333333333333337,
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Speaker({'obj_type': 'speaker', 'meta': {'is-admin': True, 'edit-count': '13927'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Davodd'}): {'article': 0.0,
'auxverb': 0.0,
'conj': 0.0,
'adverb': 0.0,
'ppron': 0.0,
'ipron': 0.0,
'preps': 0.0,
'quant': 0.0},
Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '25928'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Emeraude'}): {'article': 0.0,
'auxverb': -0.050000000000000044,
'conj': -0.15000000000000002,
'adverb': -0.050000000000000044,
'ppron': 0.0,
'ipron': 0.0,
'preps': 0.0,
'quant': -0.15000000000000002},
Speaker({'obj_type': 'speaker', 'meta': {'is-admin': False, 'edit-count': '20809'}, 'vectors': [], 'owner': <convokit.model.corpus.Corpus object at 0x00000185F14B09C8>, 'id': 'Trovatore'}): {'article': 0.0,
'auxverb': 0.0,
'conj': 0.0,
'adverb': 0.09722222222222221,
'ppron': 0.0,
'ipron': 0.0,
'preps': 0.0,
'quant': 0.08333333333333337},
...}
wiki_full.get_speakers_dataframe()
| vectors | meta.is-admin | meta.edit-count | |
|---|---|---|---|
| id | |||
| Frightner | [] | False | 1322 |
| Revizionist | [] | False | 1716 |
| Johannes003 | [] | False | 8283 |
| Michael-Billa | [] | False | 396 |
| AnonEMouse | [] | True | 13150 |
| ... | ... | ... | ... |
| {unknown-1133} | [] | False | unknown |
| Kelly Martin | [] | True | 17726 |
| Cretanpride | [] | False | 192 |
| CaveatLector | [] | False | 2196 |
| Huntster | [] | True | 27608 |
9331 rows × 3 columns
Aggregated corresponding to each of the speakers will be displayed
coord.transform(wiki_full)
<convokit.model.corpus.Corpus at 0x185f14b09c8>
Note: Some Scores might be missing
speaker_df = wiki_full.get_speakers_dataframe()
speaker_df
| vectors | meta.is-admin | meta.edit-count | meta.coord | |
|---|---|---|---|---|
| id | ||||
| Frightner | [] | False | 1322 | NaN |
| Revizionist | [] | False | 1716 | NaN |
| Johannes003 | [] | False | 8283 | NaN |
| Michael-Billa | [] | False | 396 | NaN |
| AnonEMouse | [] | True | 13150 | {'Kwork': {'article': 0.0, 'auxverb': 0.0, 'co... |
| ... | ... | ... | ... | ... |
| {unknown-1133} | [] | False | unknown | NaN |
| Kelly Martin | [] | True | 17726 | NaN |
| Cretanpride | [] | False | 192 | NaN |
| CaveatLector | [] | False | 2196 | NaN |
| Huntster | [] | True | 27608 | NaN |
9331 rows × 4 columns
speaker_df[speaker_df['meta.coord'].notna()]
| vectors | meta.is-admin | meta.edit-count | meta.coord | |
|---|---|---|---|---|
| id | ||||
| AnonEMouse | [] | True | 13150 | {'Kwork': {'article': 0.0, 'auxverb': 0.0, 'co... |
| FayssalF | [] | True | 42028 | {'Mattisse': {'article': 0.0, 'auxverb': 0.0, ... |
| Dana boomer | [] | True | 33852 | {'Montanabw': {'article': 0.05555555555555558,... |
| Capricornis | [] | False | 918 | {'Mr. Neutron': {'auxverb': 0.0, 'ppron': 0.0,... |
| Good Olfactory | [] | True | 363837 | {'Redheylin': {'auxverb': 0.0, 'adverb': 0.0, ... |
| ... | ... | ... | ... | ... |
| Szipucsu | [] | False | 95 | {'Psychonaut': {'auxverb': 0.0, 'adverb': 0.0,... |
| Mathwiz2020 | [] | True | 0 | {'Splash': {'article': 0.0, 'auxverb': 0.0, 'c... |
| Neustradamus | [] | False | 2211 | {'91.187.66.243': {'auxverb': 0.0, 'adverb': 0... |
| 91.187.66.243 | [] | False | unknown | {'Michael Hardy': {'auxverb': 0.0, 'conj': 0.0... |
| RJII | [] | False | 25810 | {'Splash': {'article': 0.0, 'auxverb': 0.0, 'c... |
673 rows × 4 columns
# get set of speakers
everyone = lambda speaker: True
admins = lambda speaker: speaker.meta["is-admin"]
nonadmins = lambda speaker: not speaker.meta["is-admin"]
split = ["is-admin"]
# compute coordination scores from each admin to everyone
print("Admins, ranked by how much they coordinate to others:")
speaker_to_admin = coord.summarize(wiki_full, everyone, admins, focus="targets")
dicti_counts_admin, dicti_counts_nonadmin = {}, {}
for spkr, score in sorted(speaker_to_admin.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
dicti_counts_admin[spkr.id] = round(score, 5)
speaker_to_nonadmin = coord.summarize(wiki_full,everyone, nonadmins, focus="targets")
for spkr, score in sorted(speaker_to_nonadmin.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
dicti_counts_nonadmin [spkr.id] = round(score, 5)
Admins, ranked by how much they coordinate to others:
pd.DataFrame(data = dicti_counts_admin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)
| Speaker name | Average Pair Wise Score | |
|---|---|---|
| 0 | Leyo | 0.27500 |
| 1 | Dlohcierekim | 0.25000 |
| 2 | Raul654 | 0.21000 |
| 3 | Stevenfruitsmaak | 0.20833 |
| 4 | Tznkai | 0.18571 |
| 5 | Magioladitis | 0.17202 |
| 6 | Casliber | 0.15774 |
| 7 | Hu12 | 0.15625 |
| 8 | Bhadani | 0.15238 |
| 9 | Guettarda | 0.15000 |
pd.DataFrame(data = dicti_counts_nonadmin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)
| Speaker name | Average Pair Wise Score | |
|---|---|---|
| 0 | Ocaasi | 0.33333 |
| 1 | Ekki01 | 0.27083 |
| 2 | PANONIAN | 0.26667 |
| 3 | Leaky_caldron | 0.25333 |
| 4 | Penyulap | 0.25000 |
| 5 | KrebMarkt | 0.25000 |
| 6 | VKokielov | 0.25000 |
| 7 | Mjb | 0.25000 |
| 8 | Armbrust | 0.23810 |
| 9 | Mdcollins1984 | 0.20089 |
admin_to_spkr = coord.summarize(wiki_full, admins, everyone)
dicti_counts_admin, dicti_counts_nonadmin = {}, {}
for spkr, score in sorted(admin_to_spkr.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
dicti_counts_admin[spkr.id] = round(score, 5)
nonadmin_to_spkr = coord.summarize(wiki_full, nonadmins, everyone)
for spkr, score in sorted(nonadmin_to_spkr.averages_by_speaker().items(),key=lambda x: x[1], reverse=True):
dicti_counts_nonadmin[spkr.id] = round(score, 5)
pd.DataFrame(data = dicti_counts_admin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)
| Speaker name | Average Pair Wise Score | |
|---|---|---|
| 0 | Gatoclass | 0.20278 |
| 1 | DarkFalls | 0.16667 |
| 2 | DO11.10 | 0.15476 |
| 3 | AnemoneProjectors | 0.15238 |
| 4 | Amalas | 0.14796 |
| 5 | Rogerd | 0.11944 |
| 6 | Josiah Rowe | 0.11490 |
| 7 | KillerChihuahua | 0.11151 |
| 8 | Guettarda | 0.11042 |
| 9 | Black Falcon | 0.10417 |
pd.DataFrame(data = dicti_counts_nonadmin.items(), columns = ['Speaker name', 'Average Pair Wise Score']).head(10)
| Speaker name | Average Pair Wise Score | |
|---|---|---|
| 0 | Howard the Duck | 0.19792 |
| 1 | Lugnuts | 0.17708 |
| 2 | WLU | 0.17542 |
| 3 | Wikidudeman | 0.16667 |
| 4 | Tagishsimon | 0.16667 |
| 5 | Wwheaton | 0.16667 |
| 6 | JJ Harrison | 0.16429 |
| 7 | PANONIAN | 0.15833 |
| 8 | Fowler&fowler | 0.15714 |
| 9 | Ed | 0.15000 |
coord.summarize(wiki_full, everyone, admins, focus="targets", summary_report=True)
{'marker_agg1': {'article': 0.015555598585512167,
'auxverb': 0.009663516499465384,
'conj': 0.013230937289510317,
'adverb': 0.017329249355186323,
'ppron': 0.0029675891397264974,
'ipron': 0.030634938792294442,
'preps': 0.010898938538928087,
'quant': 0.020082725608428467},
'marker_agg2': {'article': 0.010666995155267435,
'auxverb': 0.00965974179597438,
'conj': 0.011246236532095569,
'adverb': 0.010822727131472845,
'ppron': 0.0006280728061660206,
'ipron': 0.02307661110412696,
'preps': 0.007853887946881395,
'quant': 0.016956733937946604},
'marker_agg3': {'article': 0.010666995155267435,
'auxverb': 0.00965974179597438,
'conj': 0.011246236532095569,
'adverb': 0.010822727131472845,
'ppron': 0.0006280728061660206,
'ipron': 0.02307661110412696,
'preps': 0.007853887946881395,
'quant': 0.016956733937946604},
'agg1': 0.01504543672613146,
'agg2': 0.011363875801241392,
'agg3': 0.008886420812259174,
'count_agg1': 301,
'count_agg2': 511,
'count_agg3': 511}
coord.summarize(wiki_full, everyone, nonadmins, focus="targets", summary_report=True)
{'marker_agg1': {'article': 0.008340013138658358,
'auxverb': 0.007401284196896749,
'conj': 0.010106910427519265,
'adverb': 0.007511210484479474,
'ppron': 0.007713000391147967,
'ipron': 0.018540313759033426,
'preps': 0.00677588710100073,
'quant': 0.027115418874108555},
'marker_agg2': {'auxverb': 0.008130217413767777,
'adverb': 0.005881239202070832,
'ppron': 0.0068253670153056575,
'ipron': 0.013442437503031372,
'preps': 0.0018353311750622472,
'quant': 0.01996582337592379,
'conj': 0.013765255982772225,
'article': 0.006747629121977491},
'marker_agg3': {'auxverb': 0.008130217413767777,
'adverb': 0.005881239202070832,
'ppron': 0.0068253670153056575,
'ipron': 0.013442437503031372,
'preps': 0.0018353311750622472,
'quant': 0.01996582337592379,
'conj': 0.013765255982772225,
'article': 0.006747629121977491},
'agg1': 0.011688004796605563,
'agg2': 0.009574162598738967,
'agg3': 0.007599102233410238,
'count_agg1': 647,
'count_agg2': 1297,
'count_agg3': 1297}
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